Image recognition based on hidden Markov eigen-image models using variational Bayesian method

An image recognition method based on hidden Markov eigen-image models (HMEMs) using the variational Bayesian method is proposed and experimentally evaluated. HMEMs have been proposed as a model with two advantageous properties: linear feature extraction based on statistical analysis and size-and-location-invariant image recognition. In many image recognition tasks, it is difficult to use sufficient training data, and complex models such as HMEMs suffer from the over-fitting problem. This study aims to accurately estimate HMEMs using the Bayesian criterion, which attains high generalization ability by using prior information and marginalization of model parameters. Face recognition experiments showed that the proposed method improves recognition performance.

[1]  Douglas A. Reynolds,et al.  Comparison of background normalization methods for text-independent speaker verification , 1997, EUROSPEECH.

[2]  Roberto Pieraccini,et al.  Dynamic planar warping for optical character recognition , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Dorothy T. Thayer,et al.  EM algorithms for ML factor analysis , 1982 .

[4]  Oscar E. Agazzi,et al.  Keyword Spotting in Poorly Printed Documents using Pseudo 2-D Hidden Markov Models , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Monson H. Hayes,et al.  Maximum likelihood training of the embedded HMM for face detection and recognition , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[6]  Zoubin Ghahramani,et al.  Variational Inference for Bayesian Mixtures of Factor Analysers , 1999, NIPS.

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

[8]  Hagai Attias,et al.  Inferring Parameters and Structure of Latent Variable Models by Variational Bayes , 1999, UAI.

[9]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[10]  Yoshihiko Nankaku,et al.  Face Recognition using Hidden Markov Eigenface Models , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[11]  Mark J. F. Gales,et al.  Generalised linear Gaussian models , 2001 .

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

[13]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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