Face Recognition Attendance System using SVM.pptx

Face Recognition Attendance System using SVM technique
Uttara University
Dept. of CSE
Batch 55

1. E-Commerce Design & Development MD LIMON & Team

2. Intruduction Traditionally, student's attendances are taken manually by using attendance sheet given by the faculty in class, which is a time consuming event. Moreover, it is very difficult to verify one by one student in a large classroom environment with distributed branches whether the authenticated students are actually responding or not. FACE RECOGNITION technology is gradually evolving to auniversal biometric solution since it requires virtually zero effort from the user end while compared with other biometric options. It is accurate and allows for high enrolment and verification rates.

3. Outline- 1. Introduction 2. Background 3. Problem definition (gap finding) 4. Objectives   -  Design  -   Developing a machine learning model    - Development of a real time prototype    - Evaluating efficacy of the system 5. Methodology 6. Gantt chart 7. Expected results 8. References

4. What exactly is Facial Recognition?? A facial recognition is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database.

5. Implementation of face recognition- • The implementation of face recognition technology includes the following three stages : Image acquisition. Image processing. Face image classification and decision making.

6. Block Diagram-

7. Image acquisition- Facial-scan technology can acquire faces from almost any static camera or video system that generates images of sufficient quality and resolution. High-quality enrolment is essential to eventual verification and identification enrolment images define the facial characteristics to be used in all future authentication events.

8. Face Recognition Attendance System using SVM ## Project Overview: This dataset was created as part of a face recognition attendance system. The system employs a Support Vector Machine (SVM) algorithm for recognizing and marking attendance based on facial images. It automatically logs attendance for detected faces by comparing real-time images with pre-stored images in a database. The system aims to provide a robust and automated solution for tracking attendance, reducing manual effort and enhancing accuracy.

9. Dataset Description: The dataset contains labeled facial images of students, along with corresponding metadata (e.g., names) used for training and testing the SVM model. Images were captured in varied lighting conditions to improve the model's generalization across real-world scenarios. The system uses the HOG (Histogram of Oriented Gradients) feature extraction technique to preprocess the images before passing them to the SVM classifier for recognition. Images: High-resolution facial images stored in folders, categorized by student names. Labels: Corresponding names and email addresses of students used as identification labels. Attendance Logs: CSV file containing timestamps and attendance status (present/absent) based on real-time face recognition. Model: SVM classifier trained to distinguish faces with high accuracy.

10. Applications: Automating attendance management in educational institutions. Enhancing security by recognizing and verifying individuals. Can be extended to various other identification use cases.

11. References- Adrian Rhesa Septian Siswanto, Anto Satriyo Nugroho, Maulahikmah Galinium ," Implementation of face recognition algorithm for biometrics based time attendance system", IEEE, ICT For Smart Society (ICISS), International Conference, January 2015. Brian C. Becker, Enrique G.Ortiz , "Evaluation of Face Recognition Techniques for Application to Facebook" IEEE, 2008.

12. Thankyou