Abstract
Although the power of hashing methods has been proved in image retrieval, they cannot effectively extract discriminative features for face image retrieval as the discriminative differences in face regions are subtle and the background information interferes with the feature expression. To solve this problem, we propose an end-to-end deep hashing method with attention mechanisms to learn discriminative hash codes. Specifically, a face spatial network is designed to enhance the discrimination of face features from the spatial aspect. With a specially designed face spatial loss, it can automatically mine differentiated facial regions, and reduce the interference of background information. Furthermore, an attention-aware hash network, in which facial features could be enhanced by fusing strategy and channel attention module, is designed to learn compact and discriminative hash codes. Experimental results on two widely used datasets demonstrate the inspiring performance over several state-of-the-art hashing methods.