Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Facial Feature Points with Gabor Wavelets and Shape Models
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Tracking Facial Features using Gabor Wavelet Networks
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
Online Appearance-Based Face and Facial Feature Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Facial Feature Detection and Tracking with Automatic Template Selection
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Emblem Detections by Tracking Facial Features
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Robust facial feature tracking under varying face pose and facial expression
Pattern Recognition
International Journal of Human-Computer Studies
Face detection using look-up table based gentle adaboost
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Spatiotemporal-boosted DCT features for head and face gesture analysis
HBU'10 Proceedings of the First international conference on Human behavior understanding
Spatiotemporal features for effective facial expression recognition
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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Automatic analysis of head and facial gestures is a significant and challenging research area for human-computer interfaces. We propose a robust face-and head gesture analyzer. The analyzer exploits trajectories of facial landmark positions during the course of the head gesture or facial expression. The trajectories themselves are obtained as the output of an accurate feature detector and tracker algorithm, which uses a combination of appearance- and model-based approaches. A multi-pose deformable shape model is trained in order to handle shape variations under varying head rotations and facial expressions. Discriminative observation symbols extracted from the landmark trajectories drive a continuous HMM with mixture of Gaussian outputs and is used to recognize a subset of head gestures and facial expressions. For seven gesture classes we achieve 86.4 % recognition rate.