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PAMI
2006

Capitalize on Dimensionality Increasing Techniques for Improving Face Recognition Grand Challenge Performance

8 years 11 months ago
Capitalize on Dimensionality Increasing Techniques for Improving Face Recognition Grand Challenge Performance
This paper presents a novel pattern recognition framework by capitalizing on dimensionality increasing techniques. In particular, the framework integrates Gabor image representation, a novel multiclass Kernel Fisher Analysis (KFA) method, and fractional power polynomial models for improving pattern recognition performance. Gabor image representation, which increases dimensionality by incorporating Gabor filters with different scales and orientations, is characterized by spatial frequency, spatial locality, and orientational selectivity for coping with image variabilities such as illumination variations. The KFA method first performs nonlinear mapping from the input space to a high-dimensional feature space, and then implements the multiclass Fisher discriminant analysis in the feature space. The significance of the nonlinear mapping is that it increases the discriminating power of the KFA method, which is linear in the feature space but nonlinear in the input space. The novelty of the ...
Chengjun Liu
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PAMI
Authors Chengjun Liu
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