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Evaluation of texture and geometry for dimensional facial expression recognition

Zhang, Ligang, Tjondronegoro, Dian W., & Chandran, Vinod (2011) Evaluation of texture and geometry for dimensional facial expression recognition. In 2011 International Conference on Digital Image Computing : Techniques and Applications (DICTA 2011), 6-8 December 2011, Sheraton Noosa Resort & Spa, Noosa, QLD.

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Abstract

Facial expression recognition (FER) algorithms mainly focus on classification into a small discrete set of emotions or representation of emotions using facial action units (AUs). Dimensional representation of emotions as continuous values in an arousal-valence space is relatively less investigated. It is not fully known whether fusion of geometric and texture features will result in better dimensional representation of spontaneous emotions. Moreover, the performance of many previously proposed approaches to dimensional representation has not been evaluated thoroughly on publicly available databases. To address these limitations, this paper presents an evaluation framework for dimensional representation of spontaneous facial expressions using texture and geometric features. SIFT, Gabor and LBP features are extracted around facial fiducial points and fused with FAP distance features. The CFS algorithm is adopted for discriminative texture feature selection. Experimental results evaluated on the publicly accessible NVIE database demonstrate that fusion of texture and geometry does not lead to a much better performance than using texture alone, but does result in a significant performance improvement over geometry alone. LBP features perform the best when fused with geometric features. Distributions of arousal and valence for different emotions obtained via the feature extraction process are compared with those obtained from subjective ground truth values assigned by viewers. Predicted valence is found to have a more similar distribution to ground truth than arousal in terms of covariance or Bhattacharya distance, but it shows a greater distance between the means.

Impact and interest:

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ID Code: 44131
Item Type: Conference Paper
Keywords: facial expression recognition, dimensional space, continuous value, SIFT, FAP
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2011 IEEE
Deposited On: 23 Aug 2011 08:09
Last Modified: 07 Jun 2012 16:40

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