HIGH-SPEED FACE RECOGNITION BASED ON DISCRETE COSINE TRANSFORM AND RBF NEURAL NETWORKS

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HIGH-SPEED FACE RECOGNITION BASED ON DISCRETE COSINE TRANSFORM AND RBF NEURAL NETWORKS

Abstract:

In this paper, an efficient method for high-speed face recognition based on the discrete cosine transform (DCT), the Fisher’s linear discriminant (FLD) and radial basis function (RBF) neural networks is presented. First, the dimensionality of the original face image is reduced by using the DCT and the large area illumination variations are alleviated by discarding the first few low-frequency DCT coefficients. Next, the truncated DCT coefficient vectors are clustered using the proposed clustering algorithm. This process makes the subsequent FLD more efficient. After implementing the FLD, the most discriminating and invariant facial features are maintained and the training samples are clustered well. As a consequence, further parameter estimation for the RBF neural networks is fulfilled easily which facilitates fast training in the RBF neural networks. Simulation results show that the proposed system achieves excellent performance with high training and recognition speed, high recognition rate as well as very good illumination robustness.

TABLE OF CONTENT
Title page- – – – – – – – – i
Approval page – – – – – – – -ii
Dedication – – – – – – – – -iii
Acknowledgement – – – – – – – -iv
Abstract – – – – – – – – – -v
Table of content – – – – – – – -vi

CHAPTER ONE
INTRODUCTION – – – – – – – -1
1.0 Background of the study – – – – -1
1.1 Statement of the problem – – – – -5
1.2 Purpose of the study – – – – – -6
1.3 Significance of the study – – – – -8
1.4 Research questions – – – – – -9
1.5 Scope of the study – – – – – – -10

CHAPTER TWO

LITERATURE REVIEW – – – – – – -11

CHAPTER THREE

Research methodology – – – – – – -39
Design of study – – – – – – – -40
Area of study – – – – – – – – -40
Population of the study – – – – – – -41
Sample and sampling techniques – – – – -41
Instrument for data collection – – – – -41
Method of data collection – – – – – -42
Method of data analysis – – – – – – -43

CHAPTER FOUR

Presentation, analysis and interpretation of data – -48
Discussion of findings – – – – – – -56

CHAPTER FIVE

Summary of findings – – – – – – -60
Conclusion – – – – – – – – -61
Recommendations – – – – – – – -62
Suggestions for further research – – – – -64
References – – – – – – – – -65
Appendix I – – – – — – – – -68
Questionnaire. – – – – – – – -69

 HIGH-SPEED FACE RECOGNITION BASED ON DISCRETE COSINE TRANSFORM AND RBF NEURAL NETWORKS

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