Face recognition based on separable lattice 2-D HMMS using variational bayesian method
Kei Sawada, Akira Tamamori, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda · 2012
This paper proposes an image recognition technique based on separable lattice 2-D HMMs (SL2D-HMMs) using the variational Bayesian method. SL2D-HMMs have been proposed to reduce the effect of geometric variations, e.g., size and location. The maximum likelihood criterion had previously been used in training SL2D-HMMs. However, in many image recognition tasks, it is difficult to use sufficient training data, and it suffers from the over-fitting problem. A higher generalization ability based on model marginalization is expected by applying the Bayesian criterion and useful prior information on model parameters can be utilized as prior distributions. Experiments on face recognition indicated that the proposed method improved image recognition.