Status-aware projection metric learning for kinship verification

摘要

This paper develops a status-aware projection metric learning (SPML) method for facial image-based kinship verification, especially for the parent-child kinship. Kinship verification for parent-child is considered to be an asymmetrical metric process, in that parents and children are associated with different status where the parents are priority known to be significantly older than the children. Accordingly, an SPML is proposed to address the asymmetric metric learning. The proposed SPML takes advantage of two status-specific projections to capture the significant appearance commonality between parents and children, respectively, which generally outperforms the one Mahalanobis distance metric. Extensive experimental results and comparisons with state-of-the-art approaches and baseline methods demonstrate the effectiveness of the proposed SPML for kinship verification.