Prototype-Based Discriminative Feature Learning for Kinship Verification

• Published in: IEEE transactions on cybernetics 2015-11, Vol.45 (11), p.2535-2545
• Main Authors: Yan, Haibin, Lu, Jiwen, Zhou, Xiuzhuang
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Biometry - methods; Child; Cybernetics; Databases, Factual; Discriminative learning; Face; Face - anatomy & histology; Family; Feature extraction; feature learning; Female; Human; Humans; Image Processing, Computer-Assisted - methods; kinship verification; Learning; Learning systems; Machine Learning; Male; Mathematical analysis; Methods; mid-level feature representation; Optimization; Pattern Recognition, Automated - methods; prototype; Representations; soft biometrics; Support vector machines; Tasks; Training; Vectors; Vectors (mathematics); Young Adult
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2014.2376934

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 show the superior performance of the proposed methods over both the state-of-the-art kinship verification methods and human ability in our kinship verification task.