Multi-view pedestrian samples have so high intra-class variance that multi-view pedestrian classification suffers from high classification error. To solve this problem, a novel multi-view pedestrian recognition algorithm is proposed in this paper based on non-negative matrix factorization (NMF) and least square.Firstly, by using the NMF, the subspace of multi-view pedestrian samples is acquired and base vectors are extracted. Secondly, by introducing the collaborative representation the sparse presentation of the subspace is performed and then constrained by the least square,sparse coefficients are obtained. Finally, multi-viewpoint classification is completed by using sparse coefficients based on the nearest subspace rule. The comparison experimental results on the self-established multi-view pedestrian dataset show that the proposed method outperforms several state-of-the-art methods in terms of accuracy and effectiveness.

non-negative matrix factorization; non-negative least square; sparse representation; multi-view classification