Gabor-based multi-scale Illumination Normalization model for face recognition
A novel Gabor-based multi-scale illumination normalization (GMSIN) model is proposed and applied to face recognition. GMSIN uses Total variation under different norm constraints. It removes the lighting effect in two scale parts of image and fuses the multi-scaled illumination invariant features. Then a bank of Gabor filters is built to extract lighting invariant Gabor face representations. Finally the higher-order statistical relationships among variables of samples are extracted for classifier. According to the experiments on the large scale CAS-PEAL face database, GMSIN could outperform conventional algorithms when they face most outliers (lighting, expression, masking etc.).