Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11506
Title: Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression
Authors: Gaus, YFBA
Meng, H
Jan, A
Zhang, F
Turabzadeh, S
Keywords: Emotion recognition;Feature extraction;Mel frequency cepstral coefficient;Testing;Videos;Wavelet transforms
Issue Date: 2015
Publisher: IEEE
Citation: Proceedings of 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia, (4-8 May 2015)
Abstract: Automatic affective dimension recognition from facial expression continuously in naturalistic contexts is a very challenging research topic but very important in human-computer interaction. In this paper, an automatic recognition system was proposed to predict the affective dimensions such as Arousal, Valence and Dominance continuously in naturalistic facial expression videos. Firstly, visual and vocal features are extracted from image frames and audio segments in facial expression videos. Secondly, a wavelet transform based digital filtering method is applied to remove the irrelevant noise information in the feature space. Thirdly, Partial Least Squares regression is used to predict the affective dimensions from both video and audio modalities. Finally, two modalities are combined to boost overall performance in the decision fusion process. The proposed method is tested in the fourth international Audio/Visual Emotion Recognition Challenge (AVEC2014) dataset and compared to other state-of-the-art methods in the affect recognition sub-challenge with a good performance.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7284859
http://bura.brunel.ac.uk/handle/2438/11506
DOI: http://dx.doi.org/10.1109/FG.2015.7284859
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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