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- Type of Document: M.Sc. Thesis
- Language: English
- Document No: 40185 (55)
- University: Sharif University of Technology, International Campus, Kish Island
- Department: Science and Engineering
- Advisor(s): Kasaei, Shohreh
- Abstract:
- Biometrics has been long known to recognize persons based on their physical and behavioral characteristics. Face recognition (FR) is one of such biometrics that has received a considerable attention in recent years both from the industry and research communities. As the boosting framework has shown good performance in face recognition, it has been adopted in this work. This thesis deals with pattern recognition methods such as linear discriminant analysis (LDA) and machine learning approaches such as boosting which are integrated to overcome the technical limitation of existing FR methods. However, LDA-based methods often suffer from the so-called “small-sample-size” (SSS) problem arising from the small number of available training samples compared to the dimensionality of the sample space. To overcome this problem, a new LDA method is utilized which is particularly robust against the small-sample-size problem compared to the traditional one used in LDA. The AdaBoost technique is utilized to generalize a set of simple FR subproblems and their corresponding LDA solutions and combines the results from the multiple, relatively weak, LDA solutions to form a strong solution. The comparative experimental result on FERET database demonstrates that the proposed boosting method achieves more accurate results over the individual algorithms
- Keywords:
- Reinforcement Learning ; Linear Discriminant Analysis ; Adaboost Algorithm ; Face Recognition ; Small-Sample-Size (SSS)Problem
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