Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Boosting encoded dynamic features for facial expression recognition
Pattern Recognition Letters
Integrated Computer-Aided Engineering
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Boosted multi-resolution spatiotemporal descriptors for facial expression recognition
Pattern Recognition Letters
Toward Practical Smile Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Local binary patterns for multi-view facial expression recognition
Computer Vision and Image Understanding
An enhanced independent component-based human facial expression recognition from video
IEEE Transactions on Consumer Electronics
Polichotomies on imbalanced domains by one-per-class compensated reconstruction rule
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Research in automatic facial expression recognition has permitted the development of systems discriminating between the six prototypical expressions, i.e. anger, disgust, fear, happiness, sadness and surprise, in frontal video sequences. Achieving high recognition rate often implies high computational costs that are not compatible with real time applications on limited-resource platforms. In order to have high recognition rate as well as computational efficiency, we propose an automatic facial expression recognition system using a set of novel features inspired by statistical moments. Such descriptors, named as statisticallike moments extract high order statistic from texture descriptors such as local binary patterns. The approach has been successfully tested on the second edition of Cohn-Kanade database, showing a computational advantage and achieving a performance recognition rate comparable than methods based on different descriptors