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Heterogeneous Hashing Network for Face Retrieval Across Image and Video Domains

In this paper, we present a heterogeneous hashing network to generate effective and compact hash representations of both face images and face videos for face retrieval across image and video domains. The network contains an image branch and a video branch to project face images and videos into a common space, respectively. Then, the non-linear hash functions are learned in the common space to obtain the corresponding binary hash representations. The network is trained with three loss functions: 1) the Fisher loss; 2) the softmax loss; and 3) the triplet ranking loss. The Fisher loss uses the difference form of within-class and between-class scatter and is appropriate for the mini-batch-based optimization method. The Fisher loss together with the softmax loss is exploited to enhance the discriminative power of the common space. The triplet ranking loss is enforced on the final binary hash representations to improve retrieval performance. Experiments on a large-scale face video dataset and two challenging TV-series datasets demonstrate the effectiveness of the proposed method.