Face recognition based on extended separable lattice 2-D HMMS
Kumaki Keisuke, Yoshihiko Nankaku, Keiichi Tokuda · 2012
This paper proposes an extension of separable lattice 2-D hidden Markov models (SL-HMMs) for dealing with image rotation and local deformation. It is important to reduce the effect of geometrical variations in image recognition, e.g., location, size, and rotation. SLHMMs are one of the most efficient structures to accomplish invariance to size and location variations. However, since SL-HMMs only have one state sequence in each direction, they cannot deal with rotation or local deformation. The proposed models have state sequences corresponding to all rows and columns of an input image, and the complicated state alignments can represent rotation and local deformation. The effectiveness of the proposed models was demonstrated in face recognition experiments.