Active shape models—their training and application
Computer Vision and Image Understanding
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dealing with occlusions in the eigenspace approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Online Facial Expression Recognition Based on Personalized Galleries
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Real time facial expression recognition in video using support vector machines
Proceedings of the 5th international conference on Multimodal interfaces
Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Research of robust facial expression recognition under facial occlusion condition
AMT'11 Proceedings of the 7th international conference on Active media technology
Full body acting rehearsal in a networked virtual environment-a case study
Presence: Teleoperators and Virtual Environments
Towards a dynamic expression recognition system under facial occlusion
Pattern Recognition Letters
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Descriptions of three methods for reconstructing incomplete facial expressions using principal component analysis are given, projection to the model plane, single component projection and replacement by the conditional mean --- the facial expressions being represented by feature points. It is established that one method gives better reconstruction accuracy than the others. This method is used on a systematic reconstruction problem, the reconstruction of occluded top and bottom halves of faces. The results indicate that occluded-top expressions can be reconstructed with little loss of expression recognition --- occluded-bottom expressions are reconstructed less accurately but still give comparable performance to human rates of facial expression recognition.