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- Title
Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics.
- Authors
Lin, Chuang; Jiang, Jifeng; Zhao, Xuefeng; Pang, Meng; Ma, Yanchun
- Abstract
Kernel Locality Preserving Projection (KLPP) algorithm can effectively preserve the neighborhood structure of the database using the kernel trick. We have known that supervised KLPP (SKLPP) can preserve within-class geometric structures by using label information. However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP. In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP) is proposed in this paper, which can maximize the class separability in kernel learning. The proposed method maps the data from the original space to a higher dimensional kernel space using a data-dependent kernel. The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function. Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data. Experimental results on ORL, Yale, AR, and Palmprint databases showed the effectiveness of the proposed method.
- Subjects
HUMAN facial recognition software; PALMPRINT recognition; BIOMETRIC identification; KERNEL (Mathematics); MATHEMATICAL optimization; NONLINEAR analysis
- Publication
Mathematical Problems in Engineering, 2015, Vol 2015, p1
- ISSN
1024-123X
- Publication type
Article
- DOI
10.1155/2015/421671