Deep learning features for robust facial kinship verification

Abstract

Facial automatic kinship verification is a novel challenging research problem in computer vision. It performs the automatic examining of the facial attributes and expecting whether two persons have a biological kin relation or not. In this study, the authors introduce a novel learning method for kinship verification which consists of four main stages. (i) A discrete cosine transform network (DCTNet) applied to each face image in order to extract the most significant inherited facial features through convolutional layers based on 2D DCT filter bank. (ii) The response of the last layer is binarised and partitioned into non-overlapping block-wise histograms. (iii) A tied rank normalisation is used to eliminate the disparity of histogram vectors of DCTNet. (iv) The last stage is to distinguish between the different pairs. The distances between data points in the same classes (positive pairs) are as small as possible, while the distances are as large as possible between data points in different classes (negative pairs). Experiments are conducted on three public databases (UBKinFace, KinFaceW-I, and KinFaceW-II). They show significant performance improvements compared to state-of-the-art methods.