A multi-perspective holistic approach to Kinship Verification in the Wild

The automatic verification of kinship is a challenging problem that recently attracted much interest from the research community. It consists in telling whether two individuals are related or not, based on the analysis of their facial images. This is a challenging task since it has to deal with differences in race, gender and age between subjects. In addition, the unpredictable amount of genetic information shared by relatives reflects into individuals showing different degrees of facial similarity. Kinship recognition in the wild introduces more difficulties, since the images to be analyzed can have low resolutions, different illuminations, resolutions, face orientations, expressions and occlusions. Due to the characteristics of the image in analysis, which highly reduces the discriminative power of local features, we address kinship recognition in the wild with a multi-perspective holistic approach. The image pairs to be labeled as kin or non-kin are first characterized by selecting the most relevant variables from the combination of different global textural features. The resulting feature vectors are then used to feed an SVM classifier, which has been assessed on the Kinship Face in the Wild (KinFaceW) dataset over different sub-classes of parent-child relationships. Experimental results show that our method provides, on the same data, optimal accuracies with respect to other approaches and outperforms the recognition abilities of human beings.