Robust face recognition using locally adaptive sparse representation

This paper presents a block-based face-recognition algorithm based on a sparse linear-regression subspace model via locally adaptive dictionary constructed from past observable data (training samples). The local features of the algorithm provide an immediate benefit - the increase in robustness level to various registration errors. Our proposed approach is inspired by the way human beings often compare faces when presented with a tough decision: we analyze a series of local discriminative features (do the eyes match? how about the nose? what about the chin?...) and then make the final classification decision based on the fusion of local recognition results. In other words, our algorithm attempts to represent a block in an incoming test image as a linear combination of only a few atoms in a dictionary consisting of neighboring blocks in the same region across all training samples. The results of a series of these sparse local representations are used directly for recognition via either maximum likelihood fusion or a simple democratic majority voting scheme. Simulation results on standard face databases demonstrate the effectiveness of the proposed algorithm in the presence of multiple mis-registration errors such as translation, rotation, and scaling.