Abstract: For the shooting of different monitoring vision， the person re-identification task is greatly misjudged within the intra-class （the same person）， and the ambiguity inter-class （similar persons） causes low differentiation. A method （Dual-branch Feature Concatenation Network， DFCNet） that integrates depth features is proposed in this paper， the deep features of the network are assembled to complement the feature information and obtain discrimination feature， and the BN layer replaces the full connection layer after the global average pooling layer of backbone network， the network is trained with label smoothing cross-entropy loss function， which solves the problem of within the intra-class changes and the ambiguity inter-class with low differentiation. To verify the effectiveness of the proposed method， the validation was performed on the Market1501， DukeMTMC-reID public datasets， which Rank-1 and mAP can reach 95.8% and 94.3% on Market1501. The proposed method has good performance in dealing with intra-class miscalculation and inter-class difficulty discrimination， and the recognition accuracy outperforms the state-of-the-art algorithms of comparison.

Key words: pattern recognition, person re-identification, feature extraction, double-branch feature concatenation