Consistency-Exclusivity Regularized Deep Metric Learning for General Kinship Verification

While encouraging results have been made so far to advance kinship verification by using facial images, learning a robust genetic similarity measure remains challenging, especially in the setting of general kinship verification, wherein the gender labels of the test samples are unknown in advance. In this paper we present a deep metric learning method with a carefully designed two-stream neural network to jointly learn a pair of deep embeddings for parent-child images. In particular, the deep embeddings are first modeled to explicitly consist of the common and individual components, and then two additional constraints are introduced in deep metric learning: 1) value-aware consistency on the common components, and 2) position-aware exclusivity on the individual components. The proposed hierarchical consistency-exclusivity regularization enables our deep metric learning to harness the sharable and complementary patterns inherent in parent-child images. Empirically, we show improved performance over state of the art metric learning solutions to general kinship verification on two benchmarks.