Kernel Learning Algorithms for Face Recognition

Kernel Learning Algorithms for Face Recognition

Jun-Bao Li, Shu-Chuan Chu, Jeng-Shyang Pan

Language: English

Pages: 225

ISBN: 1493952129

Format: PDF / Kindle (mobi) / ePub


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),

ECBC (2003) Eigenbands fusion for frontal face recognition. In: Proceedings of IEEE international conference on image processing, vol 1, pp 665–668 14. Yang Q, Ding XQ (2003) Symmetrical principal component analysis and its application in face recognition. Chin J Comput 26:1146–1151 15. Torres L, Lorente L, Vilà J (2000) Face recognition using self-eigenfaces. In: Proceedings of international symposium on image/video communications over fixed and mobile networks. Rabat, Morocco, pp 44–47 16.

P, Deshmukh A (2011) Human perception-based color image segmentation using comprehensive learning particle swarm optimization. J Inf Hiding Multimedia Sig Process 2(3):227–235 66. Hu D, Feng G, Zhou Z (2007) Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition. Pattern Recogn 40(1):339–342 References 43 67. Zhi R, Ruan Q (2008) Facial expression recognition based on two-dimensional discriminant locality preserving projections. Neurocomputing

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 %)

Neurocomputing 73(16–18):3334–3337 22. Cheng J, Liu Q, Lua H, Chen YW (2005) Supervised kernel locality preserving projections for face recognition. Neurocomputing 67:443–449 23. Zhao H, Sun S, Jing Z, Yang J (2006) Local structure based supervised feature extraction. Pattern Recogn 39(8):1546–1550 24. Li JB, Pan JS, Chu SC (2008) Kernel class-wise locality preserving projection. Inf Sci 178(7):1825–1835 25. Veerabhadrappa M, Rangarajan L (2010) Diagonal and secondary diagonal locality preserving

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