Algorithms for clustering data
Algorithms for clustering data
EMPATH: face, emotion, and gender recognition using holons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Face Recognition: From Theory to Applications
Face Recognition: From Theory to Applications
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Hidden Markov Model-Based Approach to Sequential Data Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture 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
First steps towards automatic recognition of spontaneous facial action units
Proceedings of the 2001 workshop on Perceptive user interfaces
Toward computers that recognize and respond to user emotion
IBM Systems Journal
Face-specific processing in the human fusiform gyrus
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
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
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
On finding differences between faces
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Identity Management in Face Recognition Systems
Biometrics and Identity Management
Dynamic tongueprint: A novel biometric identifier
Pattern Recognition
On the results of the first mobile biometry (MOBIO) face and speaker verification evaluation
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
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
Recognition of facial expressions by cortical multi-scale line and edge coding
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Measuring the degree of face familiarity based on extended NMF
ACM Transactions on Applied Perception (TAP)
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As confirmed by recent neurophysiological studies, the use of dynamic information is extremely important for humans in visual perception of biological forms and motion. Apart from the mere computation of the visual motion of the viewed objects, the motion itself conveys far more information, which helps understanding the scene. This paper provides an overview and some new insights on the use of dynamic visual information for face recognition. In this context, not only physical features emerge in the face representation, but also behavioral features should be accounted. While physical features are obtained from the subject's face appearance, behavioral features are obtained from the individual motion and articulation of the face. In order to capture both the face appearance and the face dynamics, a dynamical face model based on a combination of Hidden Markov Models is presented. The number of states (or facial expressions) are automatically determined from the data by unsupervised clustering of expressions of faces in the video. The underlying architecture closely recalls the neural patterns activated in the perception of moving faces. Experimental results obtained from real video image data show the feasibility of the proposed approach.