Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition

GaoYun AN  JiYing WU  QiuQi RUAN  

IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.5   pp.1573-1576
Publication Date: 2008/05/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.5.1573
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
face recognition,  total variation,  Gabor analysis,  KPCA,  

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In order to overcome the drawback of TVQI and to utilize the property of dimensionality increasing techniques, a novel model for Kernel TV-based Quotient Image employing Gabor analysis is proposed and applied to face recognition with only one sample per subject. To deal with illumination outliers, an enhanced TV-based quotient image (ETVQI) model is first adopted. Then for preprocessed images by ETVQI, a bank of Gabor filters is built to extract features at specified scales and orientations. Lastly, KPCA is introduced to extract final high-order and nonlinear features of extracted Gabor features. According to experiments on the CAS-PEAL face database, our model could outperform Gabor-based KPCA, TVQI and Gabor-based TVQI when they face most outliers (illumination, expression, masking etc.).