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Emotion Recognition by Two View SVM_2K Classifier on Dynamic Facial Expression Features

Meng, H; Romera-Paredes, B; Bianchi-Berthouze, N; (2011) Emotion Recognition by Two View SVM_2K Classifier on Dynamic Facial Expression Features. In: Proceedings of the 9th IEEE International Conference on Face and Gusture Recognition (FG'11), FERA 2011 - Workshop on Facial Expression Recognition and Analysis Challenge. (pp. pp. 854-859). IEEE: Piscataway, US. Green open access

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A novel emotion recognition system has been proposed for classifying facial expression in videos. Firstly, two types of basic facial appearance descriptors were extracted. The first type of descriptor, called Motion History Histogram (MHH), was used to detect temporal changes of each pixels of the face. The second type of descriptor, called Histogram of Local Binary Patterns (LBP), was applied to each frame of the video and was used to capture local textural patterns. Secondly, based on these two basic types of descriptors, two new dynamic facial expression features called MHH_EOH and LBP_MCF were proposed. These two features incorporate both dynamic and local information. Finally, the Two View SVK_2K classifier was built to integrate these two dynamic features in an efficient way. The experimental results showed that this method outperformed the baseline results set by the FERA'11 challenge.

Type: Proceedings paper
Title: Emotion Recognition by Two View SVM_2K Classifier on Dynamic Facial Expression Features
ISBN-13: 9781424491407
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/FG.2011.5771362
Publisher version: http://dx.doi.org/10.1109/FG.2011.5771362
Language: English
Additional information: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Emotion recognition, facial expression, motion feature
UCL classification: UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Psychology and Language Sciences (Division of) > UCL Interaction Centre
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/1104630
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