Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition
Zhang, Ligang, Tjondronegoro, Dian W., & Chandran, Vinod (2012) Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition. In Zhang, Jian, Schonfeld, Dan, & Feng, David Dagan (Eds.) Proceedings of the 2012 IEEE Conference on Multimedia and Expo, IEEE, Melbourne, pp. 1027-1032.
Feature extraction and selection are critical processes in developing facial expression recognition (FER) systems. While many algorithms have been proposed for these processes, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER. This paper addresses this issue by proposing a unified framework for a comparative study on the widely used texture (LBP, Gabor and SIFT) and geometric (FAP) features, using Adaboost, mRMR and SVM feature selection algorithms. Our experiments on the Feedtum and NVIE databases demonstrate the benefits of fusing geometric and texture features, where SIFT+FAP shows the best performance, while mRMR outperforms Adaboost and SVM. In terms of computational time, LBP and Gabor perform better than SIFT. The optimal combination of SIFT+FAP+mRMR also exhibits a state-of-the-art performance.
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|Item Type:||Conference Paper|
|Keywords:||Facial expression recognition, performance comparison, feature selection, Gabor, SIFT|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science|
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||01 Mar 2012 16:50|
|Last Modified:||07 Aug 2012 18:31|
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