This project entitled “Human Face Recognition Using Image Processing” submitted by Munem Shahrear Himel ID Kangkan Bar ID and Mehedi Hassan Bappy ID to the Department of Computer Science and Engineering, Daffodil International University, has been accepted as satisfactory in partial fulfillment of the degree requirement B.Sc. We hereby declare that we have carried out this project ourselves under the supervision of Anup Majumder, Lecturer in CSE Department, Daffodil International University. Facial recognition is a personal identification system that uses an individual's personal characteristics to identify a person's identity.
The human face recognition system is basically two-stage, such as face detection, where this process is carried out very quickly in humans, in addition to the conditions in which objects are located at short distances, the next role is to identify a person's face. . For this project we use some keywords like face recognition, Eigen face, PCA, python, OpenCV. For extension, there are a large number of applications from this face recognition project, this project can be extended to detect different parts and sizes in different parts of the face.
Human face recognition using image processing is a process that takes a picture or video of a human face and compares it to other images in a database. We are interested in this project as we have been through several works in this region. Human face recognition software, when using different images, the individual must deal with different angles and different facial expressions.
The use of facial recognition for human identification is most beneficial for identification purposes because it is easy to recognize someone's face and because of the reason a mask can hide using it.
Report Layout
The system can be installed in busy places such as airports, railway or bus stations to detect human faces. If any face looks suspicious, the system can set an internal alarm. This system can help emotions related to improving the processing of emotional information-research people.
Introduction
Background
Research Methodology
Design Specification
Implementation and Testing
Conclusions and Future Scope
BACKGROUND
- Introduction
- Literature Review
- Comparative Studies
- Scope of The Problem
- Challenges
On paper [7] The basic image obtained by the PCA depends solely on the pairwise relationship between pixels in the image database. In paper [2], Eigenspace-based face detection is integrated into the most successful method for computational facial recognition from digital images. Starting with the eigenfaces algorithm, several eigenspace-based methods are supported to detect the face.
Contribution [5] They differ mainly by the type of projection method used (standard, differential or eigenspace of the kernel) in the dedicated projection algorithm, by the use of natural or differential images before/after projection and by the standard of similarity matching or classification. method consecrated. The purpose of this paper is to present an independent comparative study between some of the main eigenspace-based approaches. In the article [7] In this study, the theoretical aspects and simulators implement databases and the Yale Faces database with databases, different classes and different images, and FERETs, multiple classes in a class and different images with different images.
It is difficult because even if there is generality in the mouth, in most cases it can change depending on age, skin color and facial expression. The first step is a classified target that indicates whether the intentional image has a binary value of yes or no as input and output, indicating that there is no face in the image. The second step is the purpose of membrane localization, the purpose of which is to take an image as an import and take the position of the position, such as a carbon box with the position of a face or face (x, y, width, height).
The table below shows the expected work to achieve the required results. Our project "Human Face Recognition Using Image Processing" will be done to make it very challenging for us about the face. For proper implementation, our human face must be properly identified and not only the person's face, but also what it shows.
If we cannot complete the time, it will be a big hurdle for us. So we will share our time and project work to complete the whole project and complete all the work. So we needed some skill and we achieved that skill to complete our project. We discussed all the issues and decided to do the right thing.
RESEARCH METHODOLOGY
- Introduction
- Methodology
- Data Flow Model
- Activity Diagram
- ER Diagram
- Use Case Model
The data flow model basically shows the flow of data and its movements within the system, from database to system. The next time, this data is used to recognize the face and the user's ID is displayed on the monitor, if known. When the system boots up and starts collecting data, it attempts to match the captured faces to the database using the Haar Cascade classifier.
Then the system highlights the face with the right tagline and starts creating activity history for the face. A user means that the administrator can only use the system to add or delete users from the database. But the user needs to add his login id and login password to enter the system.
DESIGN SPECIFICATION
Front-end Design
Python
Back-end Design
Camera Frame and Result
- Camera Frame and Result (Unknown)
Implementation Requirements
IMPLEMENTATION AND TESTING
- Implementation of Database
- User Registration from Page
- The Trainer
- Testing Implementation
- Acceptance Testing
- Test Results and Reports
- System Testing
- User Acceptance Testing
The administrator just needs to put the user information in hand and the user just needs to sit behind the registration camera (the camera that will only use for registration purpose). The face matrix is used to match the faces in front of the CCTV camera. We try different faces and also similar faces to crack the system or find the weakness of the system.
Although the system takes almost 300 pictures for a person or face, it gives a fantastic result. The test case was much better than other recognition programs. It can recognize faster than others. Now the beta version of the software will offer some beta developers for their feedback.
CONCULASION AND FUTURE SCOPE
Discussion and Conclusion
Scope for Further Developments
APPENDIX
Appendix A: Project Reflection
Appendix B: Related Issues
