Independent Gabor Analysis of Multiscale Total Variation-Based Quotient Image

A new algorithm for independent Gabor analysis of multiscale total variation-based quotient image is proposed and applied to face recognition with only one sample per subject here. With our preproposed multiscale TV-based quotient image (TVQI) model, the large-scale and small-scale features are firstly fused to produce the most expressive lighting invariant face. Then a bank of Gabor filters is built to extract lighting invariant Gabor face representations with specified scales and orientations. Last, an information maximization algorithm is adopted to extract higher-order statistical relationships among variables of samples for classifier. According to the experiments on the large-scale CAS-PEAL face database, our approach could outperform Gabor-based ICA, Gabor-based KPCA, and TVQI when they face most outliers (lighting, expression, masking, etc.).