Title :
Neighborhood Repulsed Metric Learning for Kinship Verification
Author :
Jiwen Lu ; Xiuzhuang Zhou ; Yap-Pen Tan ; Yuanyuan Shang ; Jie Zhou
Author_Institution :
Adv. Digital Sci. Center, Singapore, Singapore
Abstract :
Kinship verification from facial images is an interesting and challenging problem in computer vision, and there are very limited attempts on tackle this problem in the literature. In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification. Motivated by the fact that interclass samples (without a kinship relation) with higher similarity usually lie in a neighborhood and are more easily misclassified than those with lower similarity, we aim to learn a distance metric under which the intraclass samples (with a kinship relation) are pulled as close as possible and interclass samples lying in a neighborhood are repulsed and pushed away as far as possible, simultaneously, such that more discriminative information can be exploited for verification. To make better use of multiple feature descriptors to extract complementary information, we further propose a multiview NRML (MNRML) method to seek a common distance metric to perform multiple feature fusion to improve the kinship verification performance. Experimental results are presented to demonstrate the efficacy of our proposed methods. Finally, we also test human ability in kinship verification from facial images and our experimental results show that our methods are comparable to that of human observers.
Keywords :
biometrics (access control); computer vision; face recognition; feature extraction; formal verification; learning (artificial intelligence); MNRML; complementary information extraction; computer vision; distance metric; facial image; feature descriptor; interclass sample; kinship verification; multiview NRML; neighborhood repulsed metric learning; Databases; Educational institutions; Face; Feature extraction; Learning systems; Measurement; Training; Face and gesture recognition; biometrics; kinship verification; metric learning; multiview learning;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2013.134