Prototype-Based Discriminative Feature Learning for Kinship Verification

IEEE Transactions on Cybernetics
Haibin YanXiuzhuang Zhou

Abstract

In this paper, we propose a new prototype-based discriminative feature learning (PDFL) method for kinship verification. Unlike most previous kinship verification methods which employ low-level hand-crafted descriptors such as local binary pattern and Gabor features for face representation, this paper aims to learn discriminative mid-level features to better characterize the kin relation of face images for kinship verification. To achieve this, we construct a set of face samples with unlabeled kin relation from the labeled face in the wild dataset as the reference set. Then, each sample in the training face kinship dataset is represented as a mid-level feature vector, where each entry is the corresponding decision value from one support vector machine hyperplane. Subsequently, we formulate an optimization function by minimizing the intraclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kin relation) with the mid-level features. To better use multiple low-level features for mid-level feature learning, we further propose a multiview PDFL method to learn multiple mid-level features to improve the verification performance. Experimental results on four publicly available kinship datasets...Continue Reading

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Citations

Feb 2, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Miguel Bordallo LopezAbdenour Hadid
Sep 23, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Naman KohliAngshul Majumdar
Jan 24, 2017·IEEE Transactions on Cybernetics·Moises DiazRejean Plamondon
Jul 12, 2018·IEEE Transactions on Pattern Analysis and Machine Intelligence·Joseph P RobinsonYun Fu

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