A method of multi-criteria set recognition based on deep feature representation.

The large variations in the angle of different camera views and illumination can change the appearance of a lot of people, which makes human re identification is still a challenging problem. Therefore, the development of robust feature descriptors and the design of discriminative distance metrics to measure similarity between pedestrian images are two key aspects of human re identification. In this paper, we propose a method to improve the performance of the re identification using depth learning and multiple metric ensembles. First, we use a variety of data sets to train the general convolutional neural network (CNN), which is used to extract the features of the training and test set after deep level. Deep architecture makes it possible for people to learn more abstract and internal features that are robust to changes in viewpoint and illumination. Then, we utilize the deep features of the training set to learn a specific distance metric and combine it with the cosine distance metric. Multi metric sets can be used to measure the similarity between different images. Finally, a large number of experiments show that our method can effectively improve the recognition performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]