An Accelerated Variational Framework for Face Expression Recognition
Wentao Fan, Nizar Bouguila · 2018
In this paper we examine the problem of the modeling of positive vectors that are naturally generated by several signal and image processing applications. We consider a statistical framework based on a Dirichlet process mixture of inverted Dirichlet distributions like the one previously proposed in [1]. The choice of inverted Dirichlet is motivated by its flexibility to model positive features. The proposed approach can be viewed as an infinite inverted Dirichlet mixture model. As compared to [1], the infinite framework is learned via an accelerated variational approach. The accelerated variational approach offers a good compromise between frequentist techniques and purely Bayesian inference and improves the simple variational approach used in [1]. The merits of the proposed model are validated via a challenging application that involves face expression recognition.