Discriminative Multimetric Learning for Kinship Verification

In this paper, we propose a new discriminative multimetric learning method for kinship verification via facial image analysis. Given each face image, we first extract multiple features using different face descriptors to characterize face images from different aspects because different feature descr...

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Bibliographic Details
Published in:IEEE transactions on information forensics and security 2014-07, Vol.9 (7), p.1169-1178
Main Authors: Yan, Haibin, Lu, Jiwen, Deng, Weihong, Zhou, Xiuzhuang
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer information security
Computer science
control theory
systems
Correlation
Data mining
Data processing. List processing. Character string processing
Exact sciences and technology
Face
Facial
Feature extraction
Image analysis
Learning
Learning systems
Measurement
Memory organisation. Data processing
Pattern recognition. Digital image processing. Computational geometry
Software
Training
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Summary:In this paper, we propose a new discriminative multimetric learning method for kinship verification via facial image analysis. Given each face image, we first extract multiple features using different face descriptors to characterize face images from different aspects because different feature descriptors can provide complementary information. Then, we jointly learn multiple distance metrics with these extracted multiple features under which the probability of a pair of face image with a kinship relation having a smaller distance than that of the pair without a kinship relation is maximized, and the correlation of different features of the same face sample is maximized, simultaneously, so that complementary and discriminative information is exploited for verification. Experimental results on four face kinship data sets show the effectiveness of our proposed method over the existing single-metric and multimetric learning methods.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2014.2327757