Deep Gabor convolution network for person re-identification

Person re-identification is an import problem in computer vision fields and more and more deep neural network models have been developed for representation learning in this task due to their good performance. However, compared with hand-crafted feature representations, deep learned features cannot not be interpreted easily. To meet this demand, motivated by the Gabor filters’ good interpretability and the deep neural network models’ reliable learning ability, we propose a new convolution module for deep neural networks based on Gabor function (Gabor convolution). Compared with classical convolution module, every parameter in the proposed Gabor convolution kernel has a specific meaning while classical one has not. The Gabor convolution module has a good texture representation ability and is effective when it is embedded in the low layers of a network. Besides, in order to make the proposed Gabor module meaningful, a new loss function designed for this module is proposed as a regularizer of total loss function. By embedding the Gabor convolution module to the Resnet-50 network, we show that it has a good representation learning ability for person re-identification. And experiments on three widely used person re-identification datasets show favorable results compared with the state-of-the-arts.