ISSN : 1796-2048
Volume : 1    Issue : 4    Date : July 2006

Robust Face Recognition by Hierarchical Kernel Associative Memory Models Based on Spatial
Domain Gabor Transforms
Bai-ling Zhang, Pietro Cerone and Yongsheng Gao
Page(s): 1-10
Full Text:
PDF (460 KB)

Face recognition can be studied as an associative memory (AM) problem and kernel-based AM
models have been proven efficient. In this paper, a hierarchical Kernel Associative Memory (KAM)
face recognition scheme with a multiscale Gabor transform, is proposed. The pyramidal multiscale
Gabor decomposition proposed by Nestares, Navarro, Portilla and Tabernero not only provides a
very efficient implementation of the Gabor transform in the spatial domain, but also permits a fast
reconstruction of images. In our method, face images of each person are first decomposed into
their multiscale representations by a quasicomplete Gabor transform, which are then modelled
by Kernel Associative Memories. In the recognition stage, a query face image is also represented by
a Gabor multiresolution pyramid and the reconstructions from different KAM models corresponding
to even Gabor channels are then simply summed to give the recall. The recognition scheme was
thoroughly tested using several benchmarking face datasets, including the AR faces, UMIST faces,
JAFFE faces and Yale A faces, which include different kind of face variations from occlusions, pose,
expression and illumination. The experiment results show that the proposed method demonstrated
strong robustness in recognizing faces under different conditions, particularly under occlusions,
pose alterations and expression changes.

Index Terms
biometrics, face recognition, Gabor wavelet transform, associative memory, kernel methods