Face Recognition Based on Maximum Sparse Coefficients of Object Region

摘要

Face recognition is an active topic in recognition systems, while face occlusion is one of the most challenging problems for recognition. Recently, robust sparse coding achieved the state-of-the-art performance, especially when dealing with occluded images. However, robust sparse coding is known that only guarantees the coefficient is global sparse when solving sparse coefficients. In this paper, we enable the elements in the object region to approximate global maximum by fitting the distribution of elements in the object region with successful recognition. The efficacy of the proposed approach is verified on publicly available databases. Furthermore, our method can achieve much better performance when the training samples are limited.