Advanced Joint Bayesian Method for Face Verification - Liang, Yicong; Ding, Xiaoqing; Xue, Jing-Hao (IEEE transactions on information forensics and security, 2015)
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.