Facial Expression Recognition in Video Sequences
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Boosted multi-resolution spatiotemporal descriptors for facial expression recognition
Pattern Recognition Letters
HOG-Based Decision Tree for Facial Expression Classification
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Facial expression recognition on multiple manifolds
Pattern Recognition
RSMAT: Robust simultaneous modeling and tracking
Pattern Recognition Letters
Dimensionality reduction by minimizing nearest-neighbor classification error
Pattern Recognition Letters
Face tracking with automatic model construction
Image and Vision Computing
Kalman filter-based facial emotional expression recognition
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Adaptive facial expression recognition using inter-modal top-down context
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Automatic facial expression recognition based on spatiotemporal descriptors
Pattern Recognition Letters
A feasibility study in using facial expressions analysis to evaluate player experiences
Proceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System
Hough forest-based facial expression recognition from video sequences
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP
International Journal of Computer Vision and Image Processing
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We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated with facial expressions is represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold to compute a posterior probability associated with a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89% recognition rate in a set of 333 sequences from the Cohn–Kanade database.