Dual Variational Generation for Low-Shot Heterogeneous Face Recognition
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Heterogeneous Face Recognition (HFR) is a challenging issue because of the large domain discrepancy and a lack of heterogeneous data. This paper considers HFR as a dual generation problem, and proposes a new Dual Variational Generation (DVG) framework. It generates large-scale paired heterogeneous images with the same identity from noise, for the sake of reducing the domain gap of HFR, which provides a new insight into the two challenging issues in HFR. Specifically, we first introduce a dual variational autoencoder to represent a joint distribution of paired heterogeneous images. Then, we impose a distribution alignment loss in the latent space and a pairwise identity preserving loss in the image space. These ensure that DVG can generate diverse paired heterogeneous images of the same identity. Moreover, a pairwise distance loss between the generated paired heterogeneous images contributes to the optimization of the HFR network, aiming at reducing the domain discrepancy. Significant recognition improvements are observed on four HFR databases, paving a new way to address the low-shot HFR problems.
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