It is challenging to learn efficient features in person re-identification task due to complex variations of viewpoints

illumination

pose etc.In addition

deep neural network still suffers from overfitting with a small training set.To solve these problems

a Multi-information Flow Convolutional Neural Network(MiF-CNN)is designed for person re-identification which contains a specific convolutional architecture.In this architecture

features from each convolution layer are concatenated with all subsequent convolution layers so that it can improve the flowability of feature information and the efficiency of backpropagation gradient.In this way

the proposed network can learn more discriminative features.Moreover

a combination of three loss functions is used to train proposed network.Finally

the identification accuracy is obtained by ranking the similarity of extracted features measured by Euclidean distance.Experimental results on the VIPeR and CUHK01 datasets demonstrate that MiF-CNN outperform most of existing methods of person re-identification and reduce overfitting more effectively.