Kinship verification using BSIF and LBP

Abstract

In recent years, the kinship verification has got more intention due to its potential applications like images annotation, organizing the photo albums and recognizing similarities among humans. In this paper, we propose a new approach based on the Local Binary Pattern (LBP) and Binarized Statistical Image features (BSIF) descriptors and the PML (Pyramid Multi-Level) representation for the kinship verification from the static images. In particular, we investigate the best parameters of each method (LBP, BSIF and PML) for the kinship verification, then conclude the suitable combination. The approach consists three main stages which are: (1) Face preprocessing,(2) features extraction, and (3) classification and decision (kin or non kin). The proposed approach is tested and analyzed on four public databases (Cornell KinFace, UB Kin database, KinFace-I, and KinFace-II). The obtained results of our approach have shown better performance compared to the state-of-the-art approaches