关键词: 稀疏表示, 弹性网络, 人脸识别, 粒子群算法, 骨干粒子群算法, 剪枝策略

Abstract: Due to the success of sparse representation in face classification, recent researches on Sparse Representation-based pattern Classification（SRC） indicate the best sparse solution should be a research focus of face recognition. In order to do so and for the sake of the fact that the latest particle swarm algorithm called backbone particle swarm optimization（NPSO） has promising performance advantages over the conventional particle swarm algorithms, NPSO is adopted in this study. A new feature optimization algorithm called EnNPSO is proposed in which elastic network estimate is used to estimate the fitness function of NPSO algorithm and the pruning strategy is used to improve global exploration and stability. It enhances capability of handling high dimensional and small samples. Experimental results indicate the power of the proposed algorithm EnNPSO.

Key words: sparse representation, elastic network, face recognition, Particle Swarm Optimization（PSO）, bare bones Particle Swarm Optimization（PSO）, pruning strategy