Scale-Space Decomposition and Nearest Linear Combination Based Approach for Face Recognition

Abstract

Among many illumination robust approaches, scale-space decomposition based methods play an important role to reduce the lighting effects in face images. However, most of the existing scale-space decomposition methods perform recognition, based on the illumination-invariant small-scale features only. We propose a scale-space decomposition based face recognition approach that extracts the features of different scales through the TV+L1 model and wavelet transform. The approach represents a subject's face image via a subspace spanned by linear combination of the features of different scales. To decide the proper identity of the probe, the nearest neighbor (NN) approach is used to measure the similarities between a probe face image and subspace representations of gallery face images. Experiments on various benchmarks have demonstrated that the system outperforms many recognition methods in the same category.