Kinship classification through random bilinear classifier

Kinship verification has seen extensive applications in recent years, such as determination of the identity of a suspect and finding missing children. Recent research has demonstrated that machine learning algorithms can handle kinship verification fairly well. However, kinship verification has remained a major challenge in the field of computer vision, answering such questions as which parents a child in a photo belongs to. Understanding such questions would have a fundamental impact on the behavior of an artificial intelligent agent working in a human world. To address this issue, a random bilinear classifier (RBC) for kinship classification was presented by effectively exploring the dependence structure between child and parents in two aspects: similarity measure and classifier design. In addition, the stability of the random selection of samples was ensured by imposing the constraint of the similarity of those non-kin relationship image groups. Extensive experiments on TSKinFace and Family101 show that the proposed method can obtain better or comparable results.