Kernel Learning Algorithms for Face Recognition by Jun-Bao Li

By Jun-Bao Li

Kernel studying Algorithms for Face reputation covers the framework of kernel dependent face attractiveness. This publication discusses the complex kernel studying algorithms and its software on face acceptance. This booklet additionally makes a speciality of the theoretical deviation, the method framework and experiments regarding kernel dependent face popularity. integrated inside are algorithms of kernel established face reputation, and in addition the feasibility of the kernel established face reputation strategy. This publication presents researchers in trend reputation and computer studying sector with complex face popularity tools and its most up-to-date applications.

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17 Linear inseparable problem are nonlinear generalizations of principal components and subspaces, respectively. The principal curves are essentially equivalent to self-organizing maps (SOM) [49]. With the extended SOM, ViSOM preserves directly the distance information on the map along with the topology [50], which represents the nonlinear data [51] and represents a discrete principal curve or surface through producing a smooth and graded mesh in the data space. Recently, researchers proposed other manifold algorithms such as Isomap [52], locally linear embedding (LLE) [53], and locality preserving projection (LPP) [54].

Neural Network 12(6):783–789 44 2 Statistical Learning-Based Face Recognition 91. Tan X-Y, Chen S-C, Zhou Z-H et al (2006) Face recognition from a single image per person: a survey. Pattern Recogn 39(9):1725–1745 92. Malathi G, Shanthi V (2011) Statistical measurement of ultrasound placenta images complicated by gestational diabetes mellitus using segmentation approach. J Inf Hiding Multimedia Sig Process 2(4):332–343 93. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection.

Cai et al. proposed the OLPP to produce orthogonal basis functions with more power of preserving locality than LPP [58]. OLPP was reported to have more discriminating power than LPP. Yu et al. introduced a simple uncorrelated constraint into the objective function to present uncorrelated discriminant locality preserving projections (UDLPP) with the aim of preserving the within-class geometric structure but maximizing the between-class distance [59]. In order to improve the performance of LPP on the nonlinear feature extraction, researchers perform UDLPP in reproducing kernel Hilbert space to develop Kernel UDLPP for face recognition and radar target recognition.

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