EMOTION BASED MUSIC RECOMMENDATION SYSTEM
TABLE OF CONTENT
- INTRODUCTION
- LITERATURE SURVEY
- SYSTEM ANALYSIS
- FEASIBILITY STUDY
- SYSTEM REQUIREMENTS
- SYSTEM DESIGN
- IMPLEMENTATION
- SYSTEM TESTING
- INPUT DESIGN AND OUTPUTDESIGN
- SCREENSHOTS
- FUTURE WORK
- CONCLUSION
- REFERENCE
INTRODUCTION:
A face detection includes classifying image into two classes: one with faces (targets), and other containing the background (clutter) which needs to be removed. Commonalities exist between faces, they vary differently in terms of age, skin color and facial expression, this becomes difficult due to this commonality. The further problem is complicated by differing lighting conditions, image qualities and geometries, partial occlusion and disguise is also a possibility. A face detector should be able to detect the presence of any face under different set of lighting conditions in any background condition. There are six universal expressions according to Ekman they are fear, disgust surprise, anger, sadness and happiness. Face variances can be observed to recognize these expressions. For example, we can say a person is happy which can be identified as a gesture of smile by tightened eyelids and raised lips corners. A person’s internal states, social communication and intentions are indicated by change in facial expressions. Many applications in many areas like human emotions analysis, natural Human computer interaction, image retrieval and talking bots have a large effect on them by automatic facial expression detection. Face Recognition with Histogram of Oriented Gradients using CNN detection has been an impacting issue in the technological community as human beings fined facial expressions one of the most natural and powerful means to express their intentions and emotions. Last stage of the system is facial expression detection. There are basically three steps in training procedure in expression recognition systems named as feature learning, classifier construction and feature selection. Feature learning stage is first, feature selection is second and the last one is classifier construction. Only learned facial expressions variations among all features are extracted after feature learning stage. Facial expression is then represented by the best features which are chosen by feature selection. Not only maximizing inter class variation but they also should try to minimize the intra class variations of expressions not only maximizing inters class variation, but they also should minimize the intra class variations of expressions. Because same expressions of different individuals in image are far from each other in pixel’s space so minimizing the intra class variation of expressions is a problem. Techniques that can be used for facial detection are YOLO, SDD, RCNN, Faster RCNN.
Domain:
MACHINE LEARNING:
Machine learning (ML)is the study of computer algorithms that improve automatically through experience. Itis seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system:
Fig 1: Machine Learning outlook
· Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
· Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
· Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.
Other approaches have been developed which don't fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system.
DEEP LEARNING:
Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower-level features. Automatically learning features at multiple levels of abstraction allow a system to learn complex functions mapping the input to the output directly from data, without depending completely on human-crafted features. Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features.
Fig 3: Deep Learning OpenCV:
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code
Keras:
Keras was developed and maintained by François Chollet, a Google engineer using four guiding principles:
· Modularity: A model can be understood as a sequence or a graph alone. All the concerns of a deep learning model are discrete components that can be combined in arbitrary ways.
· Minimalism: The library provides just enough to achieve an outcome, no frills and maximizing readability.
· Extensibility: New components are intentionally easy to add and use within the framework, intended for researchers to trial and explore new ideas.
LITERATURE SURVEY
1 Face Detection and Facial Expression Recognition System
Author: Anagha S. Dhavalikar et al
Proposed Automatic Facial Expression recognition system. In This system there are three phase 1. Face detection 2. Feature Extraction and 3. Expression recognition. The First Phase Face Detection are done by RGB Color model, lighting compensation for getting face and morphological operations for retaining required face i.e
eyes and mouth of the face. This System is also used AAM i.e Active Appearance Model Method for facial feature extraction In this method the point on the face like eye, eyebrows and mouth are located and it create a data file which gives information about model points.
Emotional Recognition from Facial Expression Analysis using Bezier Curve Fitting
Author : Yong-Hwan Lee, Woori Han and Youngseop Kim
Proposed system based on Bezier curve fitting .This system used two step for facial expression and emotion first one is detection and analysis of facial area from input original image and next phase is verification of facial emotion of characteristics feature in the region of interest .The first phase for face detection it uses color still image based on skin color pixel by initialized spatial filtering, based on result of lighting compassion then to estimate face position and facial location of eye and mouth
Using Animated Mood Pictures in Music Recommendation
Author: Arto Lehtiniemi and Jukka Holm et al
Arto Lehtiniemi and Jukka Holm et al proposed system on animated mood picture in music recommendation. On this system the user interacts with a collection of images to receive music recommendation with respect to genre of picture. This music recommendation system is developed by Nokia researched center. This system uses textual meta tags for describing the genre and audio signal processing.
Human-computer interaction using emotion recognition from facial expression.
Author: F. Abdat, C. Maaoui et al and A. Pruski et al
F. Abdat, C. Maaoui et al and A. Pruski et al. They proposed a system fully automatic facial expression and recognition system based on three step face detection, facial characteristics extraction and facial expression classification. This system proposed anthropometric model to detect the face feature point combined to shi and Thomasi method. In this method the variation of 21 distances which describe the facial feature from neutral face and the classification base on SVM (Support Vector Machine).
Emotion-based Music Recommendation by Association Discovery from Film Music.
Author: Fang-Fei Kuo et al and Suh-Yin Lee et al
Fang-Fei Kuo et al and Suh-Yin Lee et al With the growth of digital music, the development of music recommendation is helpful for users. The existing recommendation approaches are based on the users preference on music. However, sometimes, recommending music according to the emotion is needed. In this, we propose a novel model for emotion-based music recommendation, which is based on the association discovery from film music. We investigated the music feature extraction and modified the affinity graph for association discovery between emotions and music features. Experimental result shows that the proposed approach achieves 85% accuracy in average.
Moodplay: Interactive Mood-based Music Discovery and Recommendation
Author : Ivana Andjelkovic et al and John O’Donovan et al
Ivana Andjelkovic et al and John O’Donovan et al they proposed that a large body of research in recommender systems focuses on optimizing prediction and ranking. However, recent work has highlighted the importance of other aspects of the recommendations, including transparency, control and user experience in general. Building on these aspects, we introduce MoodPlay, a hybrid recommender system music which integrates content and mood-based filtering in an interactive interface. We show how MoodPlay allows the user to explore a music collection by latent affective dimensions, and we explain how to integrate user input at recommendation time with predictions based on a pre-existing user profile. Results of a user study (N=240) are discussed, with four conditions being evaluated with varying degrees of visualization, interaction and control.
An Accurate Algorithm for Generating a Music Playlist based on Facial Expressions
Author: Anukriti Dureha et al
Anukriti Dureha et al. In this he proposed Manual segregation of a playlist and annotation of songs, in accordance with the current emotional state of a user, is labor intensive and time consuming. Numerous algorithms have been proposed to automate this process. However, the existing algorithms are slow, increase the overall cost of the system by using additional hardware (e.g. EEG systems and sensors) and have less accuracy. This presents an algorithm that automates the process of generating an audio playlist, based on the facial expressions of a user, for rendering salvage of time and labor, invested in performing the process manually. The algorithm proposed aspires to reduce the overall computational time and the cost of the designed system.
Enhancing Music Recommender Systems with Personality Information and Emotional States
Author: Bruce Ferwerda et al and Markus Schedl et al
Bruce Ferwerda et al and Markus Schedl et al proposed that the initial research assumptions to improve music recommendations by including personality and emotional states. By including these psychological factors, we believe that the accuracy of the recommendation can be enhanced. The system gives attention to how people use music to regulate their emotional states, and how this regulation is related to their personality.
SOFTWARE ENVIRONMENT:
Python is a high-level, interpreted scripting language developed in the late 1980s by Guido van Rossum at the National Research Institute for Mathematics and Computer Science in the Netherlands. The initial version was published at the alt. Sources newsgroup in 1991, and version 1.0 was released in 1994.
Python 2.0 was released in 2000, and the 2.x versions were the prevalent releases until December 2008. At that time, the development team made the decision to release version 3.0, which contained a few relatively small but significant changes that were not backward compatible with the 2.x versions. Pythons 2 and 3 are very similar, and some features of Python 3 have been back ported to Python 2. But in general, they remain not quite compatible.
Both Python 2 and 3 have continued to be maintained and developed, with periodic release updates for both. As of this writing, the most recent versions available are 2.7.15 and 3.6.5. However, an official End of Life date of January 1, 2020 has been established for Python 2, after which time it will no longer be maintained. If you are a newcomer to Python, it is recommended that you focus on Python 3.
Python is still maintained by a core development team at the Institute, and Guido is still in charge, having been given the title of BDFL (Benevolent Dictator for Life) by the Python community. The name Python, by the way, derives not from the snake, but from the British comedy troupe Monty Python’s Flying Circus, of which Guido was, and presumably still is, a fan. It is common to find references to Monty Python sketches and movies scattered throughout the Python documentation.
Python is interpreted
Many languages are compiled, meaning the source code you create needs to be translated into machine code, the language of your computer’s processor, before it can be run. Programs written in an interpreted language are passed straight to an interpreter that runs them directly.
One potential downside to interpreted languages is execution speed. Programs that are compiled into the native language of the computer processor tend to run more quickly than interpreted programs. For some applications that are particularly computationally intensive, like graphics processing or intense number crunching, this can be limiting.
MACHINE LEARNING:
Machine learning (ML)is the study of computer algorithms that improve automatically through experience. Itis seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system:
· Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
· Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
· Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.
Other approaches have been developed which don't fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system.
DEEP LEARNING:
Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower-level features. Automatically learning features at multiple levels of abstraction allow a system to learn complex functions mapping the input to the output directly from data, without depending completely on human-crafted features. Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features.
Fig 3: Deep Learning
OpenCV:
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code
Keras:
Keras was developed and maintained by François Chollet, a Google engineer using four guiding principles:
· Modularity: A model can be understood as a sequence or a graph alone. All the concerns of a deep learning model are discrete components that can be combined in arbitrary ways.
· Minimalism: The library provides just enough to achieve an outcome, no frills and maximizing readability.
· Extensibility: New components are intentionally easy to add and use within the framework, intended for researchers to trial and explore new ideas.
LITERATURE SURVEY
1 Face Detection and Facial Expression Recognition System
Author: Anagha S. Dhavalikar et al
Proposed Automatic Facial Expression recognition system. In This system there are three phase 1. Face detection 2. Feature Extraction and 3. Expression recognition. The First Phase Face Detection are done by RGB Color model, lighting compensation for getting face and morphological operations for retaining required face i.e
eyes and mouth of the face. This System is also used AAM i.e Active Appearance Model Method for facial feature extraction In this method the point on the face like eye, eyebrows and mouth are located and it create a data file which gives information about model points.
Emotional Recognition from Facial Expression Analysis using Bezier Curve Fitting
Author : Yong-Hwan Lee, Woori Han and Youngseop Kim
Proposed system based on Bezier curve fitting .This system used two step for facial expression and emotion first one is detection and analysis of facial area from input original image and next phase is verification of facial emotion of characteristics feature in the region of interest .The first phase for face detection it uses color still image based on skin color pixel by initialized spatial filtering, based on result of lighting compassion then to estimate face position and facial location of eye and mouth
Using Animated Mood Pictures in Music Recommendation
Author: Arto Lehtiniemi and Jukka Holm et al
Arto Lehtiniemi and Jukka Holm et al proposed system on animated mood picture in music recommendation. On this system the user interacts with a collection of images to receive music recommendation with respect to genre of picture. This music recommendation system is developed by Nokia researched center. This system uses textual meta tags for describing the genre and audio signal processing.
Human-computer interaction using emotion recognition from facial expression.
Author: F. Abdat, C. Maaoui et al and A. Pruski et al
F. Abdat, C. Maaoui et al and A. Pruski et al. They proposed a system fully automatic facial expression and recognition system based on three step face detection, facial characteristics extraction and facial expression classification. This system proposed anthropometric model to detect the face feature point combined to shi and Thomasi method. In this method the variation of 21 distances which describe the facial feature from neutral face and the classification base on SVM (Support Vector Machine).
Emotion-based Music Recommendation by Association Discovery from Film Music.
Author: Fang-Fei Kuo et al and Suh-Yin Lee et al
Fang-Fei Kuo et al and Suh-Yin Lee et al With the growth of digital music, the development of music recommendation is helpful for users. The existing recommendation approaches are based on the users preference on music. However, sometimes, recommending music according to the emotion is needed. In this, we propose a novel model for emotion-based music recommendation, which is based on the association discovery from film music. We investigated the music feature extraction and modified the affinity graph for association discovery between emotions and music features. Experimental result shows that the proposed approach achieves 85% accuracy in average.
Moodplay: Interactive Mood-based Music Discovery and Recommendation
Author : Ivana Andjelkovic et al and John O’Donovan et al
Ivana Andjelkovic et al and John O’Donovan et al they proposed that a large body of research in recommender systems focuses on optimizing prediction and ranking. However, recent work has highlighted the importance of other aspects of the recommendations, including transparency, control and user experience in general. Building on these aspects, we introduce MoodPlay, a hybrid recommender system music which integrates content and mood-based filtering in an interactive interface. We show how MoodPlay allows the user to explore a music collection by latent affective dimensions, and we explain how to integrate user input at recommendation time with predictions based on a pre-existing user profile. Results of a user study (N=240) are discussed, with four conditions being evaluated with varying degrees of visualization, interaction and control.
An Accurate Algorithm for Generating a Music Playlist based on Facial Expressions
Author: Anukriti Dureha et al
Anukriti Dureha et al. In this he proposed Manual segregation of a playlist and annotation of songs, in accordance with the current emotional state of a user, is labor intensive and time consuming. Numerous algorithms have been proposed to automate this process. However, the existing algorithms are slow, increase the overall cost of the system by using additional hardware (e.g. EEG systems and sensors) and have less accuracy. This presents an algorithm that automates the process of generating an audio playlist, based on the facial expressions of a user, for rendering salvage of time and labor, invested in performing the process manually. The algorithm proposed aspires to reduce the overall computational time and the cost of the designed system.
Enhancing Music Recommender Systems with Personality Information and Emotional States
Author: Bruce Ferwerda et al and Markus Schedl et al
Bruce Ferwerda et al and Markus Schedl et al proposed that the initial research assumptions to improve music recommendations by including personality and emotional states. By including these psychological factors, we believe that the accuracy of the recommendation can be enhanced. The system gives attention to how people use music to regulate their emotional states, and how this regulation is related to their personality.
SOFTWARE ENVIRONMENT:
Python is a high-level, interpreted scripting language developed in the late 1980s by Guido van Rossum at the National Research Institute for Mathematics and Computer Science in the Netherlands. The initial version was published at the alt. Sources newsgroup in 1991, and version 1.0 was released in 1994.
Python 2.0 was released in 2000, and the 2.x versions were the prevalent releases until December 2008. At that time, the development team made the decision to release version 3.0, which contained a few relatively small but significant changes that were not backward compatible with the 2.x versions. Pythons 2 and 3 are very similar, and some features of Python 3 have been back ported to Python 2. But in general, they remain not quite compatible.
Both Python 2 and 3 have continued to be maintained and developed, with periodic release updates for both. As of this writing, the most recent versions available are 2.7.15 and 3.6.5. However, an official End of Life date of January 1, 2020 has been established for Python 2, after which time it will no longer be maintained. If you are a newcomer to Python, it is recommended that you focus on Python 3.
Python is still maintained by a core development team at the Institute, and Guido is still in charge, having been given the title of BDFL (Benevolent Dictator for Life) by the Python community. The name Python, by the way, derives not from the snake, but from the British comedy troupe Monty Python’s Flying Circus, of which Guido was, and presumably still is, a fan. It is common to find references to Monty Python sketches and movies scattered throughout the Python documentation.
Python is interpreted
Many languages are compiled, meaning the source code you create needs to be translated into machine code, the language of your computer’s processor, before it can be run. Programs written in an interpreted language are passed straight to an interpreter that runs them directly.
One potential downside to interpreted languages is execution speed. Programs that are compiled into the native language of the computer processor tend to run more quickly than interpreted programs. For some applications that are particularly computationally intensive, like graphics processing or intense number crunching, this can be limiting.
Python is Simple
As programming languages go, Python is relatively uncluttered, and the developers have deliberately kept it that way.
A rough estimate of the complexity of a language can be gleaned from the number of keywords or reserved words in the language. These are words that are reserved for special meaning by the compiler or interpreter because they designate specific built-in functionality of the language.
Python has a very easy-to-read syntax. Some of Python's syntax comes from C, because that is the language that Python was written in. But Python uses whitespace to delimit code: spaces or tabs are used to organize code into groups. This is different from C. In C, there is a semicolon at the end of each line and curly braces ({}) are used to group code. Using whitespace to delimit code makes Python a very easy-to-read language.
Some things that Python is often used for are:
· Web development
· Scientific programming
· Desktop GUIs
· Network programming
· Game programming
As programming languages go, Python is relatively uncluttered, and the developers have deliberately kept it that way.
A rough estimate of the complexity of a language can be gleaned from the number of keywords or reserved words in the language. These are words that are reserved for special meaning by the compiler or interpreter because they designate specific built-in functionality of the language.
Python has a very easy-to-read syntax. Some of Python's syntax comes from C, because that is the language that Python was written in. But Python uses whitespace to delimit code: spaces or tabs are used to organize code into groups. This is different from C. In C, there is a semicolon at the end of each line and curly braces ({}) are used to group code. Using whitespace to delimit code makes Python a very easy-to-read language.
Some things that Python is often used for are:
· Web development
· Scientific programming
· Desktop GUIs
· Network programming
· Game programming
How to Install Python (Environment Set-up)
In this section of the tutorial, we will discuss the installation of python on various operating systems.
In this section of the tutorial, we will discuss the installation of python on various operating systems.
SYSTEM ANALYSIS:
SYSTEM ANALYSIS:
EXISTING SYSTEM
The emotion recognition plays a major role in interaction technology. In interaction technology the verbal components only play a one third of communication and the non-verbal components plays a two third of communication. A facial emotion recognition (FER) method is used for detecting facial expressions. Facial expression plays a major role in expressing what a person feels and it expresses inner feeling and his or her mental situation or human perspective
PROPOSED SYSTEM
The human face plays an important role in knowing an individual's mood. Camera is usedto get the required input from the human face. One of the applications of this input can be for extracting the information to deduce the mood of an individual. The “emotion” derived from the input provided earlier are used to get a list of songs. This tedious task of manually Segregating or grouping songs into different lists are reduced and helps in generating an appropriate playlist based on an individual's emotional features. Facial Expression Based Music Player aims at scanning and interpreting the data and accordingly creating a playlist based the parameters provided. Thus our proposed system focus on detecting human emotions for developing emotion based music player, which are the approaches used by available music players to detect emotions, which approach our music player follows to detect human emotions and how it is better to use our system for emotion detection. A brief idea about our systems working, playlist generation and emotion classification is also given. In this project, we used pycharm tool for analysis.
SOCIAL FEASIBILITY
The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.
SYSTEM REQUIREMENTS
HARDWARE REQUIREMENTS:
The hardware requirements may serve as the basis for a contract for the implementation of the system and should therefore be a complete and consistent specification of the whole system. They are used by software engineers as the starting point for the system design. It shouls what the system do and not how it should be implemented.
• System : Pentium Dual Core.
• Hard Disk : 120 GB.
• Monitor : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram : 1 GB
SOFTWARE REQUIREMENTS:
The software requirements document is the specification of the system. It should include both a definition and a specification of requirements. It is a set of what the system should do rather than how it should do it. The software requirements provide a basis for creating the software requirements specification. It is useful in estimating cost, planning team activities, performing tasks and tracking the teams and tracking the team’s progress throughout the development activity.
• Operating system : Windows 10
• Coding Language : python
• Tool : PyCharm
• Server : Flask
SYSTEM DESIGN
DATA FLOW DIAGRAM:- The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of input data to the system, various processing carried out on this data, and the output data is generated by this system.
- The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.
- DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.
- DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.
UML DIAGRAMSUML stands for Unified Modeling Language. UML is a standardized general-purpose modeling language in the field of object-oriented software engineering. The standard is managed, and was created by, the Object Management Group.
The goal is for UML to become a common language for creating models of object oriented computer software. In its current form UML is comprised of two major components: a Meta-model and a notation. In the future, some form of method or process may also be added to; or associated with, UML.
The Unified Modeling Language is a standard language for specifying, Visualization, Constructing and documenting the artifacts of software system, as well as for business modeling and other non-software systems.
The UML represents a collection of best engineering practices that have proven successful in the modeling of large and complex systems.
The UML is a very important part of developing objects oriented software and the software development process. The UML uses mostly graphical notations to express the design of software projects.
GOALS:
The Primary goals in the design of the UML are as follows:
1. Provide users a ready-to-use, expressive visual modeling Language so that they can develop and exchange meaningful models.
2. Provide extendibility and specialization mechanisms to extend the core concepts.
3. Be independent of particular programming languages and development process.
4. Provide a formal basis for understanding the modeling language.
5. Encourage the growth of OO tools market.
6. Integrate best practices.
USE CASE DIAGRAM:
A use case diagram in the Unified Modeling Language (UML) is a type of behavioral diagram defined by and created from a Use-case analysis. Its purpose is to present a graphical overview of the functionality provided by a system in terms of actors, their goals (represented as use cases), and any dependencies between those use cases. The main purpose of a use case diagram is to show what system functions are performed for which actor. Roles of the actors in the system can be depicted.
FLOW DIAGRAM:
Flowcharts are used in designing and documenting simple processes or programs. Like other types of diagrams, they help visualize what is going on and thereby help understand a process, and perhaps also find less-obvious features within the process, like flaws and bottlenecks. There are different types of flowcharts: each type has its own set of boxes and notations. The two most common types of boxes in a flowchart are:
• A processing step, usually called activity, and denoted as a rectangular box.
• A decision, usually denoted as a diamond.
A flowchart is described as "cross-functional" when the chart is divided into different vertical or horizontal parts, to describe the control of different organizational units. A symbol appearing in a particular part is within the control of that organizational correctly locate the responsibility for unit. Across-functional flow allows the author to correctly locate the responsibility for performing an action or making a decision, and to show the responsibility of each organizational unit for different parts of a single process.
CLASS DIAGRAM:
In software engineering, a class diagram in the Unified Modelling Language (UML) is a type of static structure diagram that describes the structure of a system by showing the system's classes, their attributes, operations (or methods), and the relationships among the classes. It explains which class contains information.
SEQUENCE DIAGRAM:
A sequence diagram in Unified Modelling Language (UML) is a kind of interaction diagram that shows how processes operate with one another and in what order. It is a construct of a Message Sequence Chart. Sequence diagrams are sometimes called.
event diagrams, event scenarios, and timing diagrams.
ACTIVITY DIAGRAM:
Activity diagrams are graphical representations of workflows of stepwise activities and actions with support for choice, iteration and concurrency. In the Unified Modelling Language, activity diagrams can be used to describe the business and operational step-by-step workflows of components in a system. An activity diagram shows the overall flow of control.
IMPLEMENTATION:MODULES:
In this application I am uploading image and then using python OPENCV i am pre-processing image to extract features and then this features is applied on SVM/Deep Learning Neural Network Training Model to predict moods of user and based on user mood all songs will be detected and shown in drop down box and user can select any song and play.
All sample images are in images folder and all songs are in songs folder and u too can include new songs to that folder and given name as happy1.mp3, happy2, happy3 or sad1, sad2 etc. Like this for all categories you can add songs. Currently i am using same song for all moods.
A. FACE DETECTION
B. EMOTION CLASSIFICATION
C. MUSIC RECOMMENDATION
MODULES DESCRIPTION:
A. FACE DETECTION:
The main objective of face detection technique is to identify the face in the frame by reducing the external noises and other factors. The steps involved in the FACE DETECTION PROCESS are 1. Image pyramid 2. Histogram of Oriented Gradients 3. Linear Classifier The data that are obtained are decomposed into the sampling image using image pyramid into multiple scales. The use of this technique is simply to extract features while reducing the noise and the other factors. The low pass image pyramid technique (also known as Gaussian pyramid) consists of smoothing the frame and subsampling it by decreasing its resolution, the process needs to be repeated a few times in order to obtain a perfect result that in the end of the process we obtain a frame similar to the original one but with a decreased resolution and an increased smoothing level.
B. EMOTION CLASSIFICATION:
When the face is successfully detected, a bounding box will be applied as an overlay on the image to extract the ROI (face) for further analysis. The extracted ROI will next be processed using the “Predictor” function which is also a called script to extract the 68 facial landmark points and save them in an array. Next, the data stored in the features array will be put in as an input into a PCA reduction code that will reduce the size of data and eliminate any correlated coordinates leaving only the necessary points as principal components. The data is a 68x2 array; 68 points, each point with coordinates on x-axes and y-axes. The array will be converted into a vector containing 136 row and 1 column. The facial landmark extraction code “Predictor” is trained with a set of images and landmark maps for each image. Figure No. 5 Flow diagram of the module - Emotion Classification The code learns how to extract the facial landmark map of a given face image based on the pixel’s intensity values indexed of each point using regression trees trained with gradient boosting algorithm. After the PCA reduction operation, the obtained data will be used for classification. A multiclass SVM with a linear kernel is employed to compare the inputted data with stored one to see in what class (emotion) it belongs. If one of the three emotions anger, fear, or surprise is detected a speed decreasing command will be executed to reduce the speed of the wheelchair to prevent the user from endangerment.
C. MUSIC RECOMMENDATION:
The input is acquired in real-time so the camera is used to capture the video and then the framing are done. The hidden markov model classification are used for processing the framed images. The frames that are obtained are considered in all frames and all pixel formats for the purpose of emotion classification . The value of each landmark in the face is calculated and is stored for future use. The efficiency of classifier is about 90-95%. so that even when there is any changes in the face due to environmental conditions the system can still dentify the face and the emotion being expressed .The emotions are then identified using the values that are obtained that are being set and from the value of the pixel that is received is being compared to that of the values that is present as threshold in the code. The values is transferred to the web service. The song are played from the emotion detected.
ALGORITHMS: Convolutional Neural Network (ConvNet/CNN):
A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.
The architecture of a ConvNet is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex. Individual neurons respond to stimuli only in a restricted region of the visual field known as the Receptive Field. A collection of such fields overlap to cover the entire visual area.
A ConvNet is able to successfully capture the Spatial and Temporal dependencies in an image through the application of relevant filters. The architecture performs a better fitting to the image dataset due to the reduction in the number of parameters involved and reusability of weights. In other words, the network can be trained to understand the sophistication of the image better.
The role of the ConvNet is to reduce the images into a form which is easier to process, without losing features which are critical for getting a good prediction. This is important when we are to design an architecture which is not only good at learning features but also is scalable to massive datasets.
Support Vector Machine (SVM)
“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well (look at the below snapshot).
Support Vectors are simply the co-ordinates of individual observation. The SVM classifier is a frontier which best segregates the two classes (hyper-plane/ line).
- The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of input data to the system, various processing carried out on this data, and the output data is generated by this system.
- The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.
- DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.
- DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.
UML stands for Unified Modeling Language. UML is a standardized general-purpose modeling language in the field of object-oriented software engineering. The standard is managed, and was created by, the Object Management Group.
The goal is for UML to become a common language for creating models of object oriented computer software. In its current form UML is comprised of two major components: a Meta-model and a notation. In the future, some form of method or process may also be added to; or associated with, UML.
The Unified Modeling Language is a standard language for specifying, Visualization, Constructing and documenting the artifacts of software system, as well as for business modeling and other non-software systems.
The UML represents a collection of best engineering practices that have proven successful in the modeling of large and complex systems.
The UML is a very important part of developing objects oriented software and the software development process. The UML uses mostly graphical notations to express the design of software projects.
GOALS:
The Primary goals in the design of the UML are as follows:
1. Provide users a ready-to-use, expressive visual modeling Language so that they can develop and exchange meaningful models.
2. Provide extendibility and specialization mechanisms to extend the core concepts.
3. Be independent of particular programming languages and development process.
4. Provide a formal basis for understanding the modeling language.
5. Encourage the growth of OO tools market.
6. Integrate best practices.
USE CASE DIAGRAM:
A use case diagram in the Unified Modeling Language (UML) is a type of behavioral diagram defined by and created from a Use-case analysis. Its purpose is to present a graphical overview of the functionality provided by a system in terms of actors, their goals (represented as use cases), and any dependencies between those use cases. The main purpose of a use case diagram is to show what system functions are performed for which actor. Roles of the actors in the system can be depicted.
FLOW DIAGRAM:
Flowcharts are used in designing and documenting simple processes or programs. Like other types of diagrams, they help visualize what is going on and thereby help understand a process, and perhaps also find less-obvious features within the process, like flaws and bottlenecks. There are different types of flowcharts: each type has its own set of boxes and notations. The two most common types of boxes in a flowchart are:
• A processing step, usually called activity, and denoted as a rectangular box.
• A decision, usually denoted as a diamond.
A flowchart is described as "cross-functional" when the chart is divided into different vertical or horizontal parts, to describe the control of different organizational units. A symbol appearing in a particular part is within the control of that organizational correctly locate the responsibility for unit. Across-functional flow allows the author to correctly locate the responsibility for performing an action or making a decision, and to show the responsibility of each organizational unit for different parts of a single process.
CLASS DIAGRAM:
In software engineering, a class diagram in the Unified Modelling Language (UML) is a type of static structure diagram that describes the structure of a system by showing the system's classes, their attributes, operations (or methods), and the relationships among the classes. It explains which class contains information.
SEQUENCE DIAGRAM:
A sequence diagram in Unified Modelling Language (UML) is a kind of interaction diagram that shows how processes operate with one another and in what order. It is a construct of a Message Sequence Chart. Sequence diagrams are sometimes called.
event diagrams, event scenarios, and timing diagrams.
ACTIVITY DIAGRAM:
Activity diagrams are graphical representations of workflows of stepwise activities and actions with support for choice, iteration and concurrency. In the Unified Modelling Language, activity diagrams can be used to describe the business and operational step-by-step workflows of components in a system. An activity diagram shows the overall flow of control.
IMPLEMENTATION:
MODULES:
In this application I am uploading image and then using python OPENCV i am pre-processing image to extract features and then this features is applied on SVM/Deep Learning Neural Network Training Model to predict moods of user and based on user mood all songs will be detected and shown in drop down box and user can select any song and play.
All sample images are in images folder and all songs are in songs folder and u too can include new songs to that folder and given name as happy1.mp3, happy2, happy3 or sad1, sad2 etc. Like this for all categories you can add songs. Currently i am using same song for all moods.
A. FACE DETECTION
B. EMOTION CLASSIFICATION
C. MUSIC RECOMMENDATION
MODULES DESCRIPTION:
A. FACE DETECTION:
The main objective of face detection technique is to identify the face in the frame by reducing the external noises and other factors. The steps involved in the FACE DETECTION PROCESS are 1. Image pyramid 2. Histogram of Oriented Gradients 3. Linear Classifier The data that are obtained are decomposed into the sampling image using image pyramid into multiple scales. The use of this technique is simply to extract features while reducing the noise and the other factors. The low pass image pyramid technique (also known as Gaussian pyramid) consists of smoothing the frame and subsampling it by decreasing its resolution, the process needs to be repeated a few times in order to obtain a perfect result that in the end of the process we obtain a frame similar to the original one but with a decreased resolution and an increased smoothing level.
B. EMOTION CLASSIFICATION:
When the face is successfully detected, a bounding box will be applied as an overlay on the image to extract the ROI (face) for further analysis. The extracted ROI will next be processed using the “Predictor” function which is also a called script to extract the 68 facial landmark points and save them in an array. Next, the data stored in the features array will be put in as an input into a PCA reduction code that will reduce the size of data and eliminate any correlated coordinates leaving only the necessary points as principal components. The data is a 68x2 array; 68 points, each point with coordinates on x-axes and y-axes. The array will be converted into a vector containing 136 row and 1 column. The facial landmark extraction code “Predictor” is trained with a set of images and landmark maps for each image. Figure No. 5 Flow diagram of the module - Emotion Classification The code learns how to extract the facial landmark map of a given face image based on the pixel’s intensity values indexed of each point using regression trees trained with gradient boosting algorithm. After the PCA reduction operation, the obtained data will be used for classification. A multiclass SVM with a linear kernel is employed to compare the inputted data with stored one to see in what class (emotion) it belongs. If one of the three emotions anger, fear, or surprise is detected a speed decreasing command will be executed to reduce the speed of the wheelchair to prevent the user from endangerment.
C. MUSIC RECOMMENDATION:
The input is acquired in real-time so the camera is used to capture the video and then the framing are done. The hidden markov model classification are used for processing the framed images. The frames that are obtained are considered in all frames and all pixel formats for the purpose of emotion classification . The value of each landmark in the face is calculated and is stored for future use. The efficiency of classifier is about 90-95%. so that even when there is any changes in the face due to environmental conditions the system can still dentify the face and the emotion being expressed .The emotions are then identified using the values that are obtained that are being set and from the value of the pixel that is received is being compared to that of the values that is present as threshold in the code. The values is transferred to the web service. The song are played from the emotion detected.
Convolutional Neural Network (ConvNet/CNN):
A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.
The architecture of a ConvNet is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex. Individual neurons respond to stimuli only in a restricted region of the visual field known as the Receptive Field. A collection of such fields overlap to cover the entire visual area.
A ConvNet is able to successfully capture the Spatial and Temporal dependencies in an image through the application of relevant filters. The architecture performs a better fitting to the image dataset due to the reduction in the number of parameters involved and reusability of weights. In other words, the network can be trained to understand the sophistication of the image better.
The role of the ConvNet is to reduce the images into a form which is easier to process, without losing features which are critical for getting a good prediction. This is important when we are to design an architecture which is not only good at learning features but also is scalable to massive datasets.
Support Vector Machine (SVM)
“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well (look at the below snapshot).
Support Vectors are simply the co-ordinates of individual observation. The SVM classifier is a frontier which best segregates the two classes (hyper-plane/ line).
Pros and Cons associated with SVM
- Pros:
- It works really well with a clear margin of separation
- It is effective in high dimensional spaces.
- It is effective in cases where the number of dimensions is greater than the number of samples.
- It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
- Cons:
- It doesn’t perform well when we have large data set because the required training time is higher
- It also doesn’t perform very well, when the data set has more noise i.e. target classes are overlapping
- SVM doesn’t directly provide probability estimates, these are calculated using an expensive five-fold cross-validation. It is included in the related SVC method of Python scikit-learn library.
- Pros:
- It works really well with a clear margin of separation
- It is effective in high dimensional spaces.
- It is effective in cases where the number of dimensions is greater than the number of samples.
- It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
- Cons:
- It doesn’t perform well when we have large data set because the required training time is higher
- It also doesn’t perform very well, when the data set has more noise i.e. target classes are overlapping
- SVM doesn’t directly provide probability estimates, these are calculated using an expensive five-fold cross-validation. It is included in the related SVC method of Python scikit-learn library.
The purpose of testing is to discover errors. Testing is the process of trying to discover every conceivable fault or weakness in a work product. It provides a way to check the functionality of components, sub assemblies, assemblies and/or a finished product It is the process of exercising software with the intent of ensuring that the Software system meets its requirements and user expectations and does not fail in an unacceptable manner. There are various types of test. Each test type addresses a specific testing requirement.
TYPES OF TESTS
Unit testing:
Unit testing involves the design of test cases that validate that the internal program logic is functioning properly, and that program inputs produce valid outputs. All decision branches and internal code flow should be validated. It is the testing of individual software units of the application .it is done after the completion of an individual unit before integration. This is a structural testing, that relies on knowledge of its construction and is invasive. Unit tests perform basic tests at component level and test a specific business process, application, and/or system configuration. Unit tests ensure that each unique path of a business process performs accurately to the documented specifications and contains clearly defined inputs and expected results.
Integration testing:
Integration tests are designed to test integrated software components to determine if they actually run as one program. Testing is event driven and is more concerned with the basic outcome of screens or fields. Integration tests demonstrate that although the components were individually satisfaction, as shown by successfully unit testing, the combination of components is correct and consistent. Integration testing is specifically aimed at exposing the problems that arise from the combination of components.
Functional test:
Functional tests provide systematic demonstrations that functions tested are available as specified by the business and technical requirements, system documentation, and user manuals.
Functional testing is centered on the following items:
Valid Input : identified classes of valid input must be accepted.
Invalid Input : identified classes of invalid input must be rejected.
Functions : identified functions must be exercised.
Output : identified classes of application outputs must be exercised.
Systems/Procedures : interfacing systems or procedures must be invoked.
Organization and preparation of functional tests is focused on requirements, key functions, or special test cases. In addition, systematic coverage pertaining to identify Business process flows; data fields, predefined processes, and successive processes must be considered for testing. Before functional testing is complete, additional tests are identified and the effective value of current tests is determined.
System Test:
System testing ensures that the entire integrated software system meets requirements. It tests a configuration to ensure known and predictable results. An example of system testing is the configuration oriented system integration test. System testing is based on process descriptions and flows, emphasizing pre-driven process links and integration points.
White Box Testing:
White Box Testing is a testing in which in which the software tester has knowledge of the inner workings, structure and language of the software, or at least its purpose. It is purpose. It is used to test areas that cannot be reached from a black box level.
Black Box Testing:
Black Box Testing is testing the software without any knowledge of the inner workings, structure or language of the module being tested. Black box tests, as most other kinds of tests, must be written from a definitive source document, such as specification or requirements document, such as specification or requirements document. It is a testing in which the software under test is treated, as a black box .you cannot “see” into it. The test provides inputs and responds to outputs without considering how the software works.
Unit Testing:
Unit testing is usually conducted as part of a combined code and unit test phase of the software lifecycle, although it is not uncommon for coding and unit testing to be conducted as two distinct phases
Test objectives:
· All field entries must work properly.
· Pages must be activated from the identified link.
· The entry screen, messages and responses must not be delayed.
Features to be tested
· Verify that the entries are of the correct format
· No duplicate entries should be allowed
· All links should take the user to the correct page.
The purpose of testing is to discover errors. Testing is the process of trying to discover every conceivable fault or weakness in a work product. It provides a way to check the functionality of components, sub assemblies, assemblies and/or a finished product It is the process of exercising software with the intent of ensuring that the Software system meets its requirements and user expectations and does not fail in an unacceptable manner. There are various types of test. Each test type addresses a specific testing requirement.
TYPES OF TESTS
Unit testing:
Unit testing involves the design of test cases that validate that the internal program logic is functioning properly, and that program inputs produce valid outputs. All decision branches and internal code flow should be validated. It is the testing of individual software units of the application .it is done after the completion of an individual unit before integration. This is a structural testing, that relies on knowledge of its construction and is invasive. Unit tests perform basic tests at component level and test a specific business process, application, and/or system configuration. Unit tests ensure that each unique path of a business process performs accurately to the documented specifications and contains clearly defined inputs and expected results.
Integration testing:
Integration tests are designed to test integrated software components to determine if they actually run as one program. Testing is event driven and is more concerned with the basic outcome of screens or fields. Integration tests demonstrate that although the components were individually satisfaction, as shown by successfully unit testing, the combination of components is correct and consistent. Integration testing is specifically aimed at exposing the problems that arise from the combination of components.
Functional test:
Functional tests provide systematic demonstrations that functions tested are available as specified by the business and technical requirements, system documentation, and user manuals.
Functional testing is centered on the following items:
Valid Input : identified classes of valid input must be accepted.
Invalid Input : identified classes of invalid input must be rejected.
Functions : identified functions must be exercised.
Output : identified classes of application outputs must be exercised.
Systems/Procedures : interfacing systems or procedures must be invoked.
Organization and preparation of functional tests is focused on requirements, key functions, or special test cases. In addition, systematic coverage pertaining to identify Business process flows; data fields, predefined processes, and successive processes must be considered for testing. Before functional testing is complete, additional tests are identified and the effective value of current tests is determined.
System Test:
System testing ensures that the entire integrated software system meets requirements. It tests a configuration to ensure known and predictable results. An example of system testing is the configuration oriented system integration test. System testing is based on process descriptions and flows, emphasizing pre-driven process links and integration points.
White Box Testing:
White Box Testing is a testing in which in which the software tester has knowledge of the inner workings, structure and language of the software, or at least its purpose. It is purpose. It is used to test areas that cannot be reached from a black box level.
Black Box Testing:
Black Box Testing is testing the software without any knowledge of the inner workings, structure or language of the module being tested. Black box tests, as most other kinds of tests, must be written from a definitive source document, such as specification or requirements document, such as specification or requirements document. It is a testing in which the software under test is treated, as a black box .you cannot “see” into it. The test provides inputs and responds to outputs without considering how the software works.
Unit Testing:
Unit testing is usually conducted as part of a combined code and unit test phase of the software lifecycle, although it is not uncommon for coding and unit testing to be conducted as two distinct phases
Test objectives:
· All field entries must work properly.
· Pages must be activated from the identified link.
· The entry screen, messages and responses must not be delayed.
Features to be tested
· Verify that the entries are of the correct format
· No duplicate entries should be allowed
· All links should take the user to the correct page.
Integration Testing
Software integration testing is the incremental integration testing of two or more integrated software components on a single platform to produce failures caused by interface defects.
The task of the integration test is to check that components or software applications, e.g. components in a software system or – one step up – software applications at the company level – interact without error.
Test Results: All the test cases mentioned above passed successfully. No defects encountered.
Acceptance Testing
User Acceptance Testing is a critical phase of any project and requires significant participation by the end user. It also ensures that the system meets the functional requirements.
Test Results:
All the test cases mentioned above passed successfully. No defects encountered.
SCREENSHOTS:
image Uploading
Detecting face in image
Detecting emotions
Playing song
FUTURE ENHANCEMENT.
The music player based on facial recognition system is highly essential for all the person in modern day life ecology. This system is further enhanced with benefit able features for upgrading in future. The methodology of enhancement in the automatic play of songs are done by detection of the facial expression. The facial expression is detected by programming interface with the RPI camera. An alternative method, based on additional emotions which is excluded in our system as disgust and fear. On this emotion included to support the playing of music automatically.
CONCLUSION
In this project, we presented a model to recommend a music based on the emotion based detected from the facial expression. This project proposed designed & developed an emotion based music recommendation system using face recognition System. Music are the one that has the power to heal any stress or any kind of emotions. Recent development promises a wide scope in developing emotion-based music recommendation system. Thus, the proposed system presents Face based emotion recognition system to detect the emotions and play music from the emotion detected.
Software integration testing is the incremental integration testing of two or more integrated software components on a single platform to produce failures caused by interface defects.
The task of the integration test is to check that components or software applications, e.g. components in a software system or – one step up – software applications at the company level – interact without error.
Test Results: All the test cases mentioned above passed successfully. No defects encountered.
Acceptance Testing
User Acceptance Testing is a critical phase of any project and requires significant participation by the end user. It also ensures that the system meets the functional requirements.
Test Results:
All the test cases mentioned above passed successfully. No defects encountered.
SCREENSHOTS:
Detecting face in image
Detecting emotions
Playing song
FUTURE ENHANCEMENT.
The music player based on facial recognition system is highly essential for all the person in modern day life ecology. This system is further enhanced with benefit able features for upgrading in future. The methodology of enhancement in the automatic play of songs are done by detection of the facial expression. The facial expression is detected by programming interface with the RPI camera. An alternative method, based on additional emotions which is excluded in our system as disgust and fear. On this emotion included to support the playing of music automatically.
CONCLUSION
In this project, we presented a model to recommend a music based on the emotion based detected from the facial expression. This project proposed designed & developed an emotion based music recommendation system using face recognition System. Music are the one that has the power to heal any stress or any kind of emotions. Recent development promises a wide scope in developing emotion-based music recommendation system. Thus, the proposed system presents Face based emotion recognition system to detect the emotions and play music from the emotion detected.
TEAM MEMBERS
VALUPADASU SATHWIKA
2003A52019
contact:8074208204.
EMMADI GAYATHRI
2003A52031contact:7386266296.
PRANATHI VADDIRAJU
2003A52041
contact:8374894094.
REVURI NAGARAJ REDDY
2003A52042
contact:8978387115.
ADDANKI THIRUMALA SATHWIKA
2003A52086
contact:9502351250.
| VALUPADASU SATHWIKA 2003A52019 |
| EMMADI GAYATHRI |
2003A52041
contact:8374894094.
| REVURI NAGARAJ REDDY 2003A52042 |
2003A52086
contact:9502351250.
This comment has been removed by the author.
ReplyDeleteI was really curious to see how accurate it would be.
ReplyDeleteIt analyzed my facial structure perfectly and gave quick results.
Face Shape Detector made the whole process super simple and fun.
Highly recommend it if you like trying personalized beauty tools.