Advanced Joint Bayesian Method for Face Verification

Generative Bayesian models have recently become the most promising framework in classifier design for face verification. However, we report in this paper that the joint Bayesian method, a successful classifier in this framework, suffers performance degradation due to its underuse of the expectation-...

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Bibliographic Details
Published inIEEE transactions on information forensics and security Vol. 10; no. 2; pp. 346 - 354
Main Authors Liang, Yicong, Ding, Xiaoqing, Xue, Jing-Hao
Format Journal Article
LanguageEnglish
Published IEEE 01.02.2015
Subjects
Bayes methods
Estimation
expectation-maximization (EM)
Face
Face verification
generative Bayesian models
Joints
Mathematical model
model training
Training
expectation-maximization (EM)
generative Bayesian models
model training
Face verification
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Summary:Generative Bayesian models have recently become the most promising framework in classifier design for face verification. However, we report in this paper that the joint Bayesian method, a successful classifier in this framework, suffers performance degradation due to its underuse of the expectation-maximization algorithm in its training phase. To rectify the underuse, we propose a new method termed advanced joint Bayesian (AJB). AJB has a good convergence property and achieves a higher verification rate than both the Joint Bayesian method and other state-of-the-art classifiers on the labeled faces in the wild face database.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2014.2375552