Face recognition based on separable lattice 2-D HMM with state duration modeling
Yoshiaki Takahashi, Akira Tamamori, Yoshihiko Nankaku, Keiichi Tokuda · 2010
This paper describes an extension of separable lattice 2-D HMMs (SL-HMMs) using state duration models for image recognition. SL-HMMs are generative models which have size and location invariances based on state transition of HMMs. However, the state duration probability of HMMs exponentially decreases with increasing duration, therefore it may not be appropriate for modeling image variations accuratelty. To overcome this problem, we employ the structure of hidden semi Markov models (HSMMs) in which the state duration probability is explicitly modeled by parametric distributions. Face recognition experiments show that the proposed model improved the performance for images with size and location variations.