Algorithms for clustering data
Algorithms for clustering data
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
A Hidden Markov Model-Based Approach to Sequential Data Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A bayesian approach to temporal data clustering using the hidden markov model methodology
A bayesian approach to temporal data clustering using the hidden markov model methodology
Using Hidden Markov Models and Wavelets for Face Recognition
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A Qualitative Hidden Markov Model for Spatio-temporal Reasoning
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Dynamic face recognition: From human to machine vision
Image and Vision Computing
Identity Management in Face Recognition Systems
Biometrics and Identity Management
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
Tracking vertex flow and model adaptation for three-dimensional spatiotemporal face analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Human face analysis: from identity to emotion and intention recognition
ICEB'10 Proceedings of the Third international conference on Ethics and Policy of Biometrics and International Data Sharing
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In this paper a novel approach to identity verification, based on the analysis of face video streams, is proposed, which makes use of both physiological and behavioral features. While physical features are obtained from the subject’s face appearance, behavioral features are obtained by asking the subject to vocalize a given sentence. The recorded video sequence is modelled using a Pseudo-Hierarchical Hidden Markov Model, a new type of HMM in which the emission probability of each state is represented by another HMM. The number of states are automatically determined from the data by unsupervised clustering of expressions of faces in the video. Preliminary results on real image data show the feasibility of the proposed approach.