Multi-scale feature combination for person re-identification

Person re-identification (Re-ID) is an instance-level task of image retrieval, and its identification accuracy depends on the distinguishable features extracted from people. However, most identification methods based on deep learning only mechanically extract distinguishable features of person images, and some important details are frequently overlooked. For scenes with substantial background differences or occlusions, the Re-ID efficiency is not high and the network scalability is not good. Here, the authors propose a multi-scale feature combination network (MFC-Net) model that combines structural feature information with global comprehensive feature information of the person images through a convolution neural network that can effectively retain distinguishing character detail information. The authors also propose a Gaussian stochastic pooling layer to solve the defects of the pooling layer. For the problem of many network parameters and weak performance, the authors propose an attentive feature convolutions layer. The authors perform many comparative experiments on three benchmark datasets. The results prove that our MFC-Net model performs well in person Re-ID and that its identification accuracy is higher than that of other investigated models.