||Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Gait Recognition
||Department of Electrical Engineering (on the job class)
In this thesis, we propose a method to extract the human gait features from the surveillance video through Gabor wavelet transformation, and then we classify these features by kernel principle component analysis (PCA) with the fractional power polynomial model. Because human gait feature extraction can be categorized into spatial and temporal domain, we will discuss the gait features in these two domains. In order not to lose any information from the surveillance video, this thesis uses the spatial-temporal silhouette of the people walking in the surveillance video, then we can have the gait features by taking silhouette convolution with Gabor based wavelet transformation. We classify these features by kernel PCA with the fractional power polynomial model. Finally, we use Mahalanobis distance to measure the similarity between the gait features. The simulation and the experiment results show that Gabor-based kernel PCA with fractional power polynomial models for Gait recognition have a better performance.
List of Figures VI
List of Tables Ⅸ
Chapter 1. Introduction
1.1 Motivation 1
1.2 Thesis Organization 4
Chapter 2. Related Work
2.1 Introduction 5
2.2 Gait Features 6
2.3 Gait Recognition Approach 7
2.4 Summary 9
Chapter 3. Proposed Method
3.1 Introduction 10
3.2 Gabor Wavelets 11
3.3 The Augmented Gabor Feature Vector 13
3.4 Kernel PCA 16
3.5 Similarity Measures and Classification Rule 18
3.6 Summary 19
Chapter 4. Experiment results
4.1 Introduction 21
4.2 Recognition versus Different similarity measures 21
4.3 Recognition versus Different degrees of Polynomial kernels 22
4.4 The USF HumanID Gait Database 26
4.5 Experiment results 28
4.6 Summary 41
Chapter 5. Conclusions and Future Works
5.1 Conclusions 44
5.2 Future Works 45
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