Separable lattice 2-D HMMS introducing state duration control for recognition of images with various variations

In this paper, an extension of separable lattice HMMs (SL-HMM) is described that introduces state duration control for dealing with images with various variations. SL-HMM are generative models that have size and location invariances based on state transition of HMMs. An extended model that has the structure of hidden semi-Markov models (HSMMs) in which the state duration probability is explicitly modeled by parametric distributions is also proposed. However, in this model, each state duration in a Markov chain is independent. It is supposed that each state duration should have a correlation. Therefore, in this paper, we propose a novel model that solves this problem by introducing variables representing the correlation among the state durations. Face recognition experiments show that the proposed model improved the recognition performance for images with size, locational, and rotational variations.

[1]  Shigeaki Watanabe,et al.  Subspace method to pattern recognition , 1973 .

[2]  Yoshihiko Nankaku,et al.  Face Recognition Based on Separable Lattice HMMS , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[3]  Jiri Matas,et al.  XM2VTSDB: The Extended M2VTS Database , 1999 .

[4]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[5]  Yoshihiko Nankaku,et al.  An extension of Separable Lattice 2-D HMMS for rotational data variations , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Yoshihiko Nankaku,et al.  Face recognition based on extended separable lattice 2-D HMMS , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Shaogang Gong,et al.  Audio- and Video-based Biometric Person Authentication , 1997, Lecture Notes in Computer Science.

[8]  Stephen E. Levinson,et al.  Continuously variable duration hidden Markov models for automatic speech recognition , 1986 .

[9]  Yoshihiko Nankaku,et al.  Face recognition based on separable lattice 2-D HMM with state duration modeling , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Philip C. Woodland,et al.  Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models , 1995, Comput. Speech Lang..

[11]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[12]  Monson H. Hayes,et al.  Hidden Markov models for face recognition , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).