Kinship classification through latent adaptive subspace

Yue Wu, Zhengming Ding, Joseph P. Robinson, Yun Fu

Abstract: We tackle the challenging kinship classification problem. Different from kinship verification, which tells two persons have certain kinship relation or not, kinship classification aims to identify the family that a person belongs to. Beyond age and appearance gap across parents and children, the difficulties of kinship classification lie in that any data of the children to be classified are unavailable in advance to help training. To handle this challenge, an auxiliary database with complete parents and children modalities is employed to uncover the parent-children latent knowledge. Specifically, we propose a Latent Adaptive Subspace learning (LAS) to uncover the shared knowledge between two modalities so that the unseen test children are implicitly modeled as latent factors for kinship classification. Moreover, person-wise and family-wise constraints are designed to enhance the individual similarity and couple the parents and children within families for discriminative features. Comprehensive experiments on two large kinship datasets show that the proposed algorithm can effectively inherit knowledge from different databases and modalities and achieve the state-of-the-art performance.