关键词: 稀疏编码, 稀疏表示, 人脸识别, 正交匹配追踪, 子空间追踪

Abstract: Subspace Pursuit（SP） algorithm needs prior knowledge of sparseness in face recognition based on sparse representation because its candidate atoms have the same number with sparseness. Against the disadvantage, this paper proposes an improved subspace pursuit algorithm. This algorithm introduces backtracking iterative optimization algorithm in atom selection to ensure the candidate atoms increase with the number of iterations simultaneously. By removing the candidate atoms with low credibility to make sure that the chosen atoms have the most similar structure with the identifying face image, to ensure that the presented algorithm can reconstruct faces well. The experimental results show that the improved algorithm have the highest recognition rate in face recognition on ORL and Yale B face database respectively compared with SP, Sparsity Adaptive Subspace Pursuit（SASP）, Orthogonal Matching Pursuit（OMP） and the OMP-cholesky algorithm employing in the Sparse Representation Classification（SRC） frame.

Key words: sparse coding, sparse representation, face recognition, Orthogonal Matching Pursuit, subspace tracking