Kinship verification from face images in discriminative subspaces of color components

Abstract

Automatic facial kinship verification is a challenging topic in computer vision due to its complexity and its important role in many applications such as finding missing children and forensics. This paper presents a Facial Kinship Verification (FKV) approach based on an automatic and more efficient two-step learning into color/texture information. Most of the proposed methods in automatic kinship verification from face images consider the luminance information only (i.e. gray-scale) and exclude the chrominance information (i.e. color) that can be helpful, as an additional cue, for predicting relationships. We explore the joint use of color-texture information from the chrominance and the luminance channels by extracting complementary low-level features from different color spaces. More specifically, the features are extracted from each color channel of the face image and fused to achieve better discrimination. We investigate different descriptors on the existing face kinship databases, illustrating the usefulness of color information, compared with the gray-scale counterparts, in seven various color spaces. Especially, we generate from each color space three subspaces projection matrices and then score fusion methodology to fuse three distances belonging to each test pair face images. Experiments on three benchmark databases, namely the Cornell KinFace, the KinFaceW (I & II) and the TSKinFace database, show superior results compared to the state of the art.