Proceedings ArticleDOI
A tied-mixture 2D HMM face recognition system
Hisham Othman,Tyseer Aboulnasr +1 more
- Vol. 2, pp 453-456
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TLDR
This paper shows that parameter tying in HMM also enhances the resolution in the case of small model, and studies the performance of the proposed 2D HMM tied-mixture system for face recognition.Abstract:
In this paper, a simplified 2D second-order hidden Markov model (HMM) with tied state mixtures is applied to the face recognition problem. The mixture of the model states is fully-tied across all models for lower complexity. Tying HMM parameters is a well-known solution for the problem of insufficient training data leading to nonrobust estimation. We show that parameter tying in HMM also enhances the resolution in the case of small model. The performance of the proposed 2D HMM tied-mixture system is studied for the face recognition problem and the expected improved robustness is confirmed.read more
Citations
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Journal ArticleDOI
A separable low complexity 2D HMM with application to face recognition
Hisham Othman,Tyseer Aboulnasr +1 more
TL;DR: A novel low-complexity separable but true 2D Hidden Markov Model (HMM) and its application to the problem of Face Recognition (FR) and the impact of key model parameters is studied.
Proceedings ArticleDOI
Face verification using D-HMM and adaptive K-means clustering
Behrouz Vaseghi,Somayeh Hashemi +1 more
TL;DR: Experimental results show the advantages of using P2D-DHMM recognizer engine instead of conventional continues HMM for face verification.
Proceedings ArticleDOI
Planar multi-classifier modelling-NN/NN for face recognition
TL;DR: The recorded results have shown the contribution of the proposed concepts for the generalization of planar models, initially based on HMMs, to other types of classifiers - neural in this case.
Book ChapterDOI
Attentive Person Selection for Human-Robot Interaction
TL;DR: A method that enables the robot to select the most attentive person into communication from multiple persons, and gives its attention to the selected person, and the approach is a common components-based HMM where all HMM states share same components.
Journal ArticleDOI
HMM-based gesture recognition for eye-swipe typing
TL;DR: In this article , three different HMM-based methods are developed, tested, and compared to the state-of-the-art performing LCSMapping algorithm with eye movement data acquired from the electrooculogram (EOG).
References
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Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
Proceedings ArticleDOI
Hidden Markov models for face recognition
TL;DR: A new method based on the extraction of 2D-DCT feature vectors is described, and the recognition results are compared with other face recognition approaches.
Journal ArticleDOI
Semi-continuous hidden Markov models for speech signals
Xuedong Huang,Mervyn Jack +1 more
TL;DR: In this article, a semi-continuous hidden Markov model with the continuous output probability density functions sharing in a mixture Gaussian density codebook is proposed, which can be considered as a special form of continuous mixture HMM model.
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