A pyramid multi-level face descriptor: application to kinship verification

Texture descriptors such as Local Binary Pattern (LBP), Local Phase Quantization (LPQ), and Histogram of Oriented Gradients (HOG) have been widely used for face image analysis. This work introduces a novel framework for image-based kinship verification able to efficiently combine local and global facial information extracted from diverse descriptors. The proposed scheme relies on two main points: (1) we model the face images using a Pyramid Multi-level (PML) representation where local descriptors are extracted from several blocks at different resolution scales; (2) we compute the covariance (second-order statistics) between diverse local features characterizing each individual block in the PML representation. This gives rise to a face descriptor with two interesting properties: (i) thanks to the PML representation, scales and face parts are explicitly encoded in the final descriptor without having to detect the facial landmarks; (ii) the covariance descriptor encodes spatial features of any type allowing the integration of several state-of-the-art texture and color features. Experiments conducted on three public kinship databases show that the proposed descriptor can outperform many state-of-the-art kinship verification algorithms and descriptors including those that are based on deep Convolutional Neural Nets.