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.
Abstract
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.