attendence PPT.pptx
- 1. GNANAMANI COLLEGE OF TECHNOLOGY AN ANDROID APPLICATION FOR SMART ATTENDANCE MANAGEMENT SYSTEM BY USING FACE RECOGNITION GUIDE NAME: Dr.R. UMAMAHESWARI.,M.E.,Ph.D., Professor/Head of the Department Presented by: S.KAVINKUMAR (620818405008) DATE: 22.09.2020
- 2. ABSTRACT Propose a system that takes the attendance of students for classroom lecture. Our system takes the attendance automatically using face recognition. However, it is difficult to estimate the attendance precisely using each result of face recognition independently because the face detection rate is not sufficiently high. Estimating the attendance precisely using all the results of face recognition obtained by continuous observation. Continuous observation improves the performance for the estimation of the attendance We constructed the lecture attendance system based on face recognition, and applied the system to classroom lecture. Finally, experiments are implemented to provide as evidence to support our plan. The result shows that continuous observation improved the performance for the estimation of the attendance.
- 3. EXISTING SYSTEM Several automated attendance systems have been proposed based on biometric recognition, barcode, QR code, and near field communication mobile device. Previous systems are inefficient in term of processing time. Low in accuracy.
- 4. DRAWBACKS Using the fingerprint scanner does not take into consideration when a person physically changes. Environment and usage can affect measurements. Systems are not 100% accurate. Require integration and/or additional hardware. Cannot be reset once compromised.
- 5. PROPOSED SYSTEM Face Recognition is natural, easy to use and does not require aid from the test subject. It is a series of several related problems which are solved step by step: To capture a picture and discern all the faces in it. Concentrate on one face at a time and understand that even if a face is turned in a strange direction or in bad lighting, it is still the same person. Determine various unique features of the face that can help in distinguishing it from the face of any other person. These characteristics could be the size eyes, length of face, skin color, etc. Compare these distinctive features of that face to all the faces of people we already know to find out the person’s name.
- 6. ADVANTAGES Greater Accuracy: 3D mapping, deep learning and other advances make FRT more reliable and harder to trick. Better Security: Research shows a 1-in-50,000 chance of a phone with touch ID being unlocked with the wrong fingerprint. With 3D facial modeling, the probability drops to nearly 1-in-1,000,000 . Convenient and Frictionless. It can be used passively without a user’s knowledge or actively such as having a person “smile for the camera.” Smarter Integration: Face recognition tools are generally easy to integrate with existing security infrastructures, saving time and cost on software redevelopment.
- 7. SYSTEM ARCHITECTURE Camera Image raster scan Face detection Tracking Background subtraction Face subspace gallery Sub image process Histogram equalization Subspace distance Identity
- 8. MODULES Image Capture Face Detection Pre-Processing
- 9. IMAGE CAPTURE The Camera is mounted at a distance from the entrance to capture the frontal images of the students and further process goes for face detection.
- 10. FACE DETECTION A proper and efficient face detection algorithm always enhances the performance of face recognition systems. Various algorithms are proposed for face detection such as Face geometry based methods, Feature Invariant methods, Machine learning based methods. Viola-Jones detection algorithm is efficient for real time application as it is fast and robust. We observed that this algorithm gives better results in different lighting conditions and we combined multiple face classifiers to achieve a better detection rates up to an angle of 30 degrees.
- 11. PRE-PROCESSING The detected face is extracted and subjected to preprocessing. This pre-processing step involves with histogram equalization of the extracted face image and is resized to 100x100. Histogram Equalization is the most common Histogram Normalization technique. This improves the contrast of the image as it stretches the range of the intensities in an image by making it more clear.
- 12. DATA FLOW DIAGRAM Capture Image Frame Detect Eyes Position Crop & Resize Face Rotate& Scale Face Compute Eigen faces Calculate Distance in the Space
- 13. LOGIN SCREEN
- 14. REGISTER SCREEN
- 17. CONCLUSION The purpose of reducing the errors that occur in the traditional attendance taking system has been achieved by implementing this automated attendance system. Face recognition systems have been presented using deep learning which exhibits robustness towards recognition of the users with accuracy of 98.3% . The result shows the capability of the system to cope with the change in posing and projection of faces. From face recognition with deep learning, it has been determined that during face detection, the problem of illumination is solved as the original image is turned into a HOG representation that captures the major features of the image regardless of image brightness.