SYNOPSIS on face recognition attendance system software
- 1. A PRESENTATION ON “FACE RECOGNITION ATTENDANCE SYSTEM SOFTWARE USING PYTHON” REPRESENTED BY : ZEESHAN ALAM SHAHENAZ KHANUM SARFE ALAM C.S.E FINAL YEAR
- 2. Introduction Face recognition is a step further to face detection. In face detection, we only detect the location of the human face in an image but in face recognition, we make a system that can identify humans. It can also be stated as, “Face recognition is a broad challenge of verifying or identifying people in pictures or videos. Big tech giants are still working to make a faster and more accurate face recognition model.”
- 3. Programming Language Python Libraries Development OpenCV : OpenCV (Open source computer vision) is a library of programming functions mainly aimed at real-time computer vision.
- 4. Steps involved in a Face Rcognition Model 1. Face Detection: Locate faces and draw bounding boxes around faces and keep the coordinates of bounding boxes. 2. Face Alignments: Normalize the faces to be consistent with the training database. 3. Feature Extraction: Extract features of faces that will be used for training and recognition tasks. 4. Face Recognition: Matching of the face against one or more known faces in a prepared database.
- 6. HOW FACE RECOGNITION SYSTEMS WORK If we look at the mirror we can see that our face has certain distinguishable landmarks which make up different facial features. Software defines these landmarks as nodal points. There are about “80 nodal points ” on a human face. Here are few nodal points that are measured by the system. ▶ Distance between the eyes ▶ Width of nose ▶ Depth of eye socket ▶ Cheekbones ▶ Jaw line ▶ chin
- 8. ADVANTAGES ▶ Automated Time Tracking System ▶ Cost-Effective ▶ Touchless Sign In System: A Post Pandemic Requirement ▶ More Accurate and Better Worker Attendance ▶ Easy To Manage
- 9. Challenges Faced By Face Recognition Systems • Illumination: It is observed that the slight changes in lighting conditions cause a significant impact on its results. • Pose: If the database is only trained on frontal face view it may result in faulty recognition or no recognition. • Facial Expressions: Different expressions of the same individual are another significant factor that needs to be taken into account. • Low Resolution: Training of recognizer must be done on a good resolution picture, otherwise the model will fail to extract features. • Aging: With increasing age, the human face features shape, lines, texture changes which are yet another challenge.
- 10. APPLICATIONS ▶ Face recognition is currently being used to make the world safer, smarter, and more convenient. ▶ There are a few use cases : • Finding Missing Person • Retail Crime • Security Identification • Identifying accounts on social media • School Attendance System • Recognizing Drivers in Cars
- 11. Advantages of LBPH over dlib Here’s a summary of the advantages of using LBPH in Dlib for face recognition: 1. Robustness to Lighting: Effective in varying lighting conditions. 2. Texture-Based Features: Captures unique face textures for better recognition. 3. Speed and Efficiency: Computationally efficient, enabling faster processing and real-time applications. 4. Scalability: Handles large datasets without significant performance loss. 5. Low Resource Requirements: Suitable for devices with limited processing power. 6. Easy Implementation: Accessible for developers with Dlib's straightforward interface. 7. Multi-Scale Analysis: Captures features at different resolutions for improved performance. 8. Good with Small Datasets: Performs well even with limited training data. 9. Flexibility: Can be combined with other techniques for enhanced accuracy. 10. Real-Time Capability: Ideal for applications requiring real-time face recognition.
- 12. CONCLUSION Face recognition attendance systems are modern utilities that are a requirement of even the post-pandemic era. These systems make employees’ attendance tracking accurate while saving costs. Such a system also adds a layer of security in the workplace. Facial recognition systems are the best modern-day solution for tracking employee hours. If your organization is still burdened by a manual attendance system, it’s time to upgrade to a facial recognition attendance system.