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Supervised Contrastive Learning for Facial Kinship Recognition

Vision-based kinship recognition aims to determine whether the face images have a kin relation. Compared to traditional solutions, the vision-based kinship recognition methods have the advantages of lower cost and being easy to implement. Therefore, such technique can be widely employed in lots of scenarios including missing children search and automatic management of family album. The Recognizing Families in the Wild (RFIW) Data Challenge provides a platform for evaluation of different kinship recognition approaches with ranked results. We propose a supervised contrastive learning approach to address three different kinship recognition tracks (i.e., kinship verification, tri-subject verification, and large-scale search-and-retrieval) announced in the RFIW 2021 with the 2021 FG. Our results on three tracks of 2021 RFIW challenge achieve the highest ranking, which demonstrate the superiority of the proposed solution.