Face recognition based on fast sparse representation of Gabor dictionary and l₀ norm

Many classic face recognition algorithms degrade sharply when they are used at identifying an individual under various conditions such as illumination, camouflage. A fast sparse representation face recognition algorithm based on Gabor dictionary and smoothed l0 norm is proposed in this paper. The Gabor filters, which could effectively extract the image local directional features at multiple scales, are less sensitive to the variations of illumination and camouflage. Smoothed l₀ algorithm requires fewer measurement values by continuously differentiable function approximation  l₀ norm. The algorithm obtains the local feature by extracting the Gabor feature, reduces the dimensions by principal component analysis (PCA) and realizes fast sparse by the  l₀ norm. Under camouflage condition, the algorithm blocks the Gabor facial feature and improves the speed of formation of the Gabor dictionary. Experimental results on AR face database show that the proposed algorithm can improve recognition speed and recognition rate and can generalize well to the face recognition, even with a few training image per class.