Kernel Learning Algorithms for Face Recognition
Jun-Bao Li, Shu-Chuan Chu, Jeng-Shyang Pan
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Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.
outperform PCA on classification. But the objective function of LPP is to minimize the local quantity, i.e., the local scatter of the projected data. This criterion cannot be guaranteed to yield a good projection for classification purposes. So, it is reasonable to enhance LPP on classification using the class information like LDA. 2.4.2 Kernel Learning-Based Face Recognition Some algorithms using the kernel trick are developed in recent years, such as kernel principal component analysis (KPCA),
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Principal Component Analysis (KPCA)-Based Face Recognition Table 4.2 Recognition performance of SKPCA Datasets Error rate (%) Training samples Banana Image F. Solar Splice Thyroid Titanic 14.2 5.4 34.2 9.4 2.2 23.2 120 (30 %) 180 (14 %) 50 (8 %) 280 (28 %) 30 (21 %) 30 (20 %) Table 4.3 Recognition Performance of SDKPCA Datasets Error rate (%) Training samples Banana Image F. Solar Splice Thyroid Titanic 13.9 5.1 32.8 9.0 2.2 24.4 120 (30 %) 180 (14 %) 50 (8 %) 280 (28 %) 30 (21 %)
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