A Hidden Markov Model approach for appearance-based 3D object recognition
Pattern Recognition Letters
Structural hidden Markov models for biometrics: Fusion of face and fingerprint
Pattern Recognition
Dynamic training using multistage clustering for face recognition
Pattern Recognition
A statistical multiresolution approach for face recognition using structural hidden Markov models
EURASIP Journal on Advances in Signal Processing
Face recognition using DCT coefficients selection
Proceedings of the 2008 ACM symposium on Applied computing
Dynamic face recognition: From human to machine vision
Image and Vision Computing
An Efficient Wavelet Based Feature Extraction Method for Face Recognition
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Component-based discriminative classification for hidden Markov models
Pattern Recognition
Reliable people tracking approach for mobile robot in indoor environments
Robotics and Computer-Integrated Manufacturing
A robust wavelet based feature extraction method for face recognition
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Recognition of human faces: from biological to artificial vision
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Hybrid generative-discriminative nucleus classification of renal cell carcinoma
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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In this paper, a new system for face recognition is proposed, based on Hidden Markov Models (HMMs) and wavelet coding. A sequence of overlapping sub-images is extracted from each face image, computing the wavelet coefficients for each of them. The whole sequence is thenmodelled by using Hidden Markov Models. The proposed method is compared with a DCT coefficients-based approach [9], showing comparable results. By using an accurate model selection procedure, we show that results proposed in [9] can be improved even more. The obtained results outperform all results presented in the literature on the Olivetti Research Laboratory (ORL) face database, reaching a 100% recognition rate. These performances proves the suitability of HMMs to deal with the new JPEG2000 image compression standard.