A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A real-time head nod and shake detector
Proceedings of the 2001 workshop on Perceptive user interfaces
Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Contextual recognition of head gestures
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
Emblem Detections by Tracking Facial Features
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Automatic prediction of frustration
International Journal of Human-Computer Studies
Robust facial feature tracking under varying face pose and facial expression
Pattern Recognition
Authentic facial expression analysis
Image and Vision Computing
Simultaneous Facial Action Tracking and Expression Recognition in the Presence of Head Motion
International Journal of Computer Vision
International Journal of Human-Computer Studies
Boosting encoded dynamic features for facial expression recognition
Pattern Recognition Letters
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Social signal processing: Survey of an emerging domain
Image and Vision Computing
Automatic nonverbal analysis of social interaction in small groups: A review
Image and Vision Computing
Detection of driver fatigue caused by sleep deprivation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
Real-time 2D+3D facial action and expression recognition
Pattern Recognition
Analysis of head and facial gestures using facial landmark trajectories
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Real time head nod and shake detection using HMMs
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Facial action recognition for facial expression analysis from static face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A probabilistic framework for modeling and real-time monitoring human fatigue
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Challenges of human behavior understanding
HBU'10 Proceedings of the First international conference on Human behavior understanding
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Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in human-computer interfaces. In this study, facial landmark points are detected and tracked over successive video frames using a robust method based on subspace regularization, Kalman prediction and refinement. The trajectories (time series) of facial landmark positions during the course of the head gesture or facial expression are organized in a spatiotemporal matrix and discriminative features are extracted from the trajectory matrix. Alternatively, appearance based features are extracted from DCT coefficients of several face patches. Finally Adaboost algorithm is performed to learn a set of discriminating spatiotemporal DCT features for face and head gesture (FHG) classification. We report the classification results obtained by using the Support Vector Machines (SVM) on the outputs of the features learned by Adaboost. We achieve 94.04% subject independent classification performance over seven FHG.