Sift Flow Based Genetic Fisher Vector Feature For Kinship Verification

Anthropology studies show that genetic features are inherited by children from their parents resulting, in visual resemblance between them. This paper presents a novel SIFT flow based genetic Fisher vector feature (SF-GFVF) which enhances the facial genetic features for kinship verification. The proposed SF-GFVE feature is derived by applying a novel similarity enhancement method based on SIFT flow and learning an inheritable transformation on the Fisher vector feature so as to enhance and encode the genetic features of parent and child image in kinship relations. In particular, the similarity enhancement method is first presented by applying the SIFT flow algorithm to the densely sampled SIFT features in order to intensify the genetic features. Further analysis shows the relation of the extracted genetic features to anthropological results and discovers interesting patterns in different kinship relations. Finally, an inheritable transformation is applied to the enhanced Fisher vector feature which is learned with the criterion of minimizing the distance between kinship samples and maximizing the distance between non-kinship samples. Experimental results on the two representative kinship databases, namely the KinFace W-I and the Kinship W-II data sets show that the proposed method is able to outperform other popular methods.