Facial Expression Recognition Paper

Published: 2021-09-12 14:10:09
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Category: Computer Science

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A PROJECT REPORT
Facial Expression Recognition
Submitted by
Vaghela Deepali (160160107103)
Vekariya Hardik (160160107109)
Zalavadiya Maulik (160160107111)
Guided by
Dr. Mahesh Goyani
In complete fulfilment for the award of the degree of
BACHELOR OF ENGINEERING
In
Computer Engineering
GOVERNMENT ENGINEERING COLLEGE MODASA
Gujarat Technological University, Ahmedabad
2019-2020
GOVERNMENT ENGINEERING COLLEGE MODASA
Department of Computer Engineering
CERTIFICATE
Date:
This is to certify that the project entitled “Facial Expression Recognition” has been out by Vaghela Deepali having enrollment no. 160160107103 under my guidance in fulfilment of the Degree of Bachelor of Engineering in In Computer Engineering 7th Semester from Gujarat Technological University during the academic year 2019-2020 and submitted on __ /__ /____.
Internal Guide Head of Department
Dr. Mahesh Goyani Prof. M. B. Chaudhari
Department of Computer Engineering Department of Computer Engineering
GEC, Modasa. GEC, Modasa.
GOVERNMENT ENGINEERING COLLEGE MODASA
Department of Computer Engineering
CERTIFICATE
Date:
This is to certify that the project entitled “Facial Expression Recognition” has been out by Vekariya Hardik having enrollment no. 160160107109 under my guidance in fulfilment of the Degree of Bachelor of Engineering in In Computer Engineering 7th Semester from Gujarat Technological University during the academic year 2019-2020 and submitted on __ /__ /____.
Internal Guide Head of Department
Dr. Mahesh Goyani Prof. M. B. Chaudhari
Department of Computer Engineering Department of Computer Engineering
GEC, Modasa. GEC, Modasa.
GOVERNMENT ENGINEERING COLLEGE MODASA
Department of Computer Engineering
CERTIFICATE
Date:
This is to certify that the project entitled “Traffic Ease Signal Timer” has been out by Zalavadiya Maulik having enrollment no. 160160107111 under my guidance in fulfilment of the Degree of Bachelor of Engineering in In Computer Engineering 7th Semester from Gujarat Technological University during the academic year 2019-2020 and submitted on __ /__ /____.
Internal Guide Head of Department
Dr. Mahesh Goyani Prof. M. B. Chaudhari
Department of Computer Engineering Department of Computer Engineering
GEC, Modasa. GEC, Modasa.
List of Figures:
Figure 2 1. Workflow Diagram 6
Figure 2 2. expression classification 7
Figure 2 3. Spiral Model 8
Figure 4 1. Use Case 12
Figure 4 2. Admin Activity 13
Figure 4 3. User Activity 14
Figure 4 4. Class Diagram 15
Figure 4 5. E-R Diagram 16
Figure 4 6. DFD Admin Level 0 17
Figure 4 7. DFD Admin Level 1 17
Figure 4 8. DFD Admin Level 2 18
Figure 4 9. DFD User Level 0 18
Figure 4 10. DFD User Level 1 19
Figure 4 11. DFD User Level 2 20
1.1 Project Summary
This presents an approach to recognize human facial expressions for human-robot interaction. For this, the facial features, especially eyes and lip are extracted and approximated using image processing the relationship between the motion of features and changes of expressions. This method can recognize the facial expression category, as well as the degree of facial expression change. Finally, the system has been implemented by issuing facial expression commands.
1.2 Project Goals and Objective
1. To develop a facial expression recognition system.
2. To experiment deep learning algorithm in computer vision fields.
3. To detect expression thus facilitating Intelligent Human-Computer Interaction.
1.3 Scope
The scope of this system is to tackle with the problems that can arise in day to day life. Some of the scopes are:
1. The system can be used to detect and track a user’s state of mind.
2. The system can be used in mini- marts, shopping center to view the feedback of the customers to enhance the business.
3. The system can be installed at busy places like airport, railway station or bus station foe detecting human faces and facial expressions of each person. If there are any faces that appeared suspicious like angry or fearful.
4. The system can also be used for educational purpose such as one can get feedback on how the student is reacting during class.
5. This system can be used foe lie detection amongst criminal; suspects during interrogation.
6. This system can help people in emotion related research to improve the processing of emotion data.
7. Clever marketing is feasible using emotional knowledge of a person which can be identified by this system.
Within the technology, we are using Python, MySQL, Image Processing, Deep Learning as Backend. For the frontend, we are using HTML5, CSS3, Bootstrap, JavaScript, jQuery, Ajax.
Image Processing:
Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays, image processing is among rapidly growing technologies. It forms core research area within engineering and computer science disciplines too.
Deep Learning:
Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.
2.3.1 User Characteristics
There are 2 groups that uses the system.
Admin:
The Admin is a main responsible person who can manage the user. Admin can watch all activities done by the user. Admin can send the notification to user any time And much more management is in the hands of Administrator Admin manage web-services.
User:
User have to install Camera And Connect with the system then Process the Face Expression of another person using this generate the report. If user is not Satisfied then he/she post complaint or give feedback to the admin.
3.1 Study Of Current System
Current face analysis systems, which are able to determine the emotional state of an individual from the facial expressions analysis, operate in three basic phases, as defined by cohn – Kanade:
1. Face detection phase,
2. Feature extraction,
3. Classification of emotions according to the selected model.
facial expression recognition (FER) is important for the transition from the instructed (stimuli) or laboratory-controlled expression to real face expression.
3.2 Problem & Weakness of Current System
Face recognition in images is one of the most challenging research issues in tracking systems (or also as part of an access system) because of different problems. Among these problems are various non-standard poses or expressions using extracted the facial parts. A simple lighting change can be often enough to lead to an incorrect classification. Thus, the robustness of the recognition method relies heavily on the strength of the extracted functions and the ability to handle both face and low-quality images.
3.3 Requirement of New System
The usage of deep learning is currently really wide. The deep learning techniques (deep neural networks) removed various problems as illumination, head pose and identity bias. The ability to learn robust features from the raw face image makes deep convolutional neural networks (DCNNs) attractive for face recognition.
3.4.2 Operational Feasibility
Operational Feasibility is a measure of how well a proposed system solves the problem and takes advantages of the opportunities identified during scope definition. The following points were considered for the project’s technical feasibility:
• The system will detect and capture the image of face.
• The captured image is then (identified which category)
3.4.3 Economic Feasibility
The purpose of economic feasibility is to determine the positive economic benefits that include quantification and identification. The system is economically feasible due to availability of all requirements such as collection of data from
• COHN-KANADE
5 Conclusion
The facial expression recognition system presented in this research work contributes a resilient face recognition model based on the mapping of behavioral characteristics with the physiological biometric characteristics. The physiological characteristics of the human face with relevance to various expressions such as happiness, sadness, fear, anger, surprise and disgust are associated with geometrical structures which restored as base matching template for the recognition system. The behavioral aspect of this system relates the attitude behind different expressions as property base. The property bases are alienated as exposed and hidden category in genetic algorithmic genes. The gene training set evaluates the expressional uniqueness of individual faces and provide a resilient expressional recognition model in the field of biometric security. The design of a novel asymmetric cryptosystem based on biometrics having features like hierarchical group security eliminates the use of passwords and smart cards as opposed to earlier cryptosystems. It requires a special hardware support like all other biometrics system. This research work promises a new direction of research in the field of asymmetric biometric cryptosystems which is highly desirable in order to get rid of passwords and smart cards completely. Experimental analysis and study show that the hierarchical security structures are effective in geometric shape identification for physiological traits.

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