High-order knowledge-based Discriminant features for kinship verification

Abstract

This research work aims to propose an effective and robust face kinship verification system by leveraging several axes, including advanced learning techniques, deep learning, and CNN networks. The contributions of this work include the use of a preprocessing method known as Multiscale Retinex with Chromaticity Preservation (MSRCP) and the Gradientfaces technique (GRF) to improve image quality and contrast enhancement. Additionally, a novel discriminative handcrafted descriptor called Histograms of dual-tree complex wavelet transform (Hist-DTCWT) is proposed. Moreover, a logistic regression for score-level fusion is used to enhance the matching process. Furthermore, a high-order knowledge-based feature using a tensor subspace model was proposed to combine multiple discriminative features. Tests were conducted out using three datasets, where the proposed method has outperformed the state the art. Typically, verification accuracies of 96.62%, 95.54% and 92.94% have been reached under Cornell KinFace, UB KinFace and TS KinFace datasets, respectively.