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.