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A robust automated attendance system using face recognition techniques PhD proposal; May 2009
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A robust automated attendance system using face recognition techniques PhD proposal; May 2009

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  1. A robust automated attendance system using face recognition techniques PhD proposal; May 2009 Gawed Nagi Supervisor Dr. Rahmita Wirza Supervisory committee Dr. Muhamad Taufik Dr. Fatimah Khalid

  2. Contents • Introduction • Background • Problem Statement • Research objectives • Basic Assumptions • Research significance • Implication • Scope • Related Work • Methodology • Work Timetable • References

  3. Background • Face recognition (FR) is an active field in computer vision and many other areas. • The need for robust and accurate face recognition systems for security and commercial purposes is increasing. • Under uncontrolled conditions, FR is still a challenging task • Partial occlusions, in face images are still an important problem for most face recognition systems • Normalizing detected face images can improve FR system robustness and reliability

  4. Problem Statement Up to date, attendance system has been taken manually which causing time waste, paper work, besides it is inaccurate. Face recognition technology can be utilized to build an automated attendance system that makes counting and identifying students much easier and convenient. Face occlusions, face scaling, and posture are still important problems in such systems.

  5. Research objectives • To establish simulation of all possible facial occlusions and face posture orientation • To determine the state-of-the-art techniques dealing with facial occlusions, face pose estimation, and face scale variation. • To build and evaluate a novel face recognition model with robustness against • Common facial occlusions (glasses, scarves, hijab, ..) • Face pose estimation • Face scale variation

  6. Basic Assumptions • Head view angle of subjects is between +15 o and – 15o • Cameras have high resolution (preferred 2896 x 1944 pixels)

  7. Research significance • Robustness is still a big concern for real face recognition systems that work on face images taken under uncontrolled conditions including facial occlusions, pose changes, and face scale variation • More research is still needed to investigate and overcome those issues • Normalizing face images and applying ICA + RFE-SVM can improve the robustness of FR system and increasing the recognition rate with face occlusions, face scaling ,and posture.

  8. Implication • AAS technology can be widely applied in private and public sectors to automatically performing the attendance in different time intervals. • Partial facial occlusions model can be applied to cope with facial occlusion problem in surveillance and security systems. • The dataset with different facial partial occlusions including Hijab will be made publicly available

  9. Scope • Neutral (normal) face expression • Controlled illumination • Frontal/ near- frontal view faces

  10. Related Work 1/2

  11. Related Work 2/2

  12. METHODOLOGY • The common scenarios of system design • Sample size • Number of images is 15 and • The total number of subjects is 270 Scenario 2 Scenario 1

  13. Cont.. • Preliminary Experiment images One sample (1420 X 528) of preliminary experiment pictures

  14. Detected faces with OpenCV code Resolution: 1420 X 528 pixels Resolution: 710 X 264 pixels

  15. Preliminary Experiment Results 1

  16. Preliminary Experiment Results 2

  17. The AAS Overview

  18. Data Collection • gathering 100 scene images for 5 different groups (each group is between 15 to 20 students) in two class sessions with 2-week time intervals. The collected images are including a round 1800 faces for approximately 75 distinct students (male and female with glasses, scarves, and Hijab, ..). • AR database, which includes two occlusion types sunglasses and scarves, will be used to test and evaluate the proposed techniques.

  19. Face Detection (FD) • FD phase has a major influence on the performance and reliability of any face recognition system • The accurate FD is, the higher better face recognition is. • Adaboost learning algorithm [Voila & Jones] is proposed for face detection due to its several techniques for effective computation of a large number of features under varying scale and location, which is important for real time performance. • Two kinds of errors may occur in face detection: • False negative when face not detected • False positive when non-face is detected

  20. The contribution • Geometry face normalization • The detected face varies in view angle, brightness, size, and etc • Those features are independent of face features and will affect the recognition Rate significantly • To improve the system recognition rate and real time efficiency as well, the research proposes an algorithm to normalize face orientation and scaling using fourier transform.

  21. Cont.. • Feature Extraction and Classification • Faces form a unique class of objects • Feature extraction plays a very important role in any pattern recognition system • This research proposes a novel approach to cope with varying types of facial occlusions based on ICA + RFE-SVM.

  22. ICA + RFE-SVM approach • Support Vector Machines (SVM) and Independent Component Analysis (ICA) are two powerful and relatively recent techniques • SVMs are classifiers which have demonstrated high generalization capabilities. ICA is a feature extraction technique which can be considered a generalization of Principal Component Analysis (PCA). • This approach is going to use ICA as a projection and feature extraction method, then selecting and ranking the features by employing Recursive Feature Elimination (RFE-SVM) so • the occluded features will gain the least weight which makes them removed. finally, classification is performed by SVM.

  23. Why ICA? Why RFE-SVM? Why ICA? Why RFE-SVM?? • Expected Results • We expect that our model will achieve a good recognition rate with robustness in terms efficiency, speed, and memory and storage requirements • A comparison between the recognition rate of faces with and without normalization method will be conducted. • A comparison between our model performance and the state-of-the-art models such as • face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel

  24. Performance Evaluation • False reject error and false accept error • True Accept Rate (TAR) and True Reject Rate (TRR) • The balanced error rate (BER). Its definition is: • Receiver Operating Characteristics (ROC)

  25. Time Table

  26. References

  27. Thank you for your attention