An ICA-based other-race effect elimination for facial expression recognition

© Springer Nature Switzerland AG 2018. Other-race effect affects the performance of multi-race facial expression recognition significantly. Though this phenomenon has been noticed by psychologists and computer vision researchers for decades, few work has been done to eliminate this influence caused...

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Main Authors: Xue, M., Duan, X., Liu, Wan-Quan, Wang, Y.
Format: Conference Paper
Published: 2018
Online Access:http://hdl.handle.net/20.500.11937/71336
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author Xue, M.
Duan, X.
Liu, Wan-Quan
Wang, Y.
author_facet Xue, M.
Duan, X.
Liu, Wan-Quan
Wang, Y.
author_sort Xue, M.
building Curtin Institutional Repository
collection Online Access
description © Springer Nature Switzerland AG 2018. Other-race effect affects the performance of multi-race facial expression recognition significantly. Though this phenomenon has been noticed by psychologists and computer vision researchers for decades, few work has been done to eliminate this influence caused by other-race effect. This work proposes an ICA-based other-race effect elimination method for 3D facial expression recognition. Firstly, the local depth features are extracted from 3D face point clouds, and then independent component analysis is used to project the features into a subspace in which the feature components are mutually independent. Second, a mutual information based feature selection method is adopted to determine race-sensitive features. Finally, the features after race-sensitive information elimination are utilized to conduct facial expression recognition. The proposed method is evaluated on BU-3DFE database, and the results reveal that the proposed method is effective to other-race effect elimination and could improve the multi-race facial expression recognition performance.
first_indexed 2025-11-14T10:47:48Z
format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T10:47:48Z
publishDate 2018
recordtype eprints
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spelling curtin-20.500.11937-713362018-12-13T09:34:32Z An ICA-based other-race effect elimination for facial expression recognition Xue, M. Duan, X. Liu, Wan-Quan Wang, Y. © Springer Nature Switzerland AG 2018. Other-race effect affects the performance of multi-race facial expression recognition significantly. Though this phenomenon has been noticed by psychologists and computer vision researchers for decades, few work has been done to eliminate this influence caused by other-race effect. This work proposes an ICA-based other-race effect elimination method for 3D facial expression recognition. Firstly, the local depth features are extracted from 3D face point clouds, and then independent component analysis is used to project the features into a subspace in which the feature components are mutually independent. Second, a mutual information based feature selection method is adopted to determine race-sensitive features. Finally, the features after race-sensitive information elimination are utilized to conduct facial expression recognition. The proposed method is evaluated on BU-3DFE database, and the results reveal that the proposed method is effective to other-race effect elimination and could improve the multi-race facial expression recognition performance. 2018 Conference Paper http://hdl.handle.net/20.500.11937/71336 10.1007/978-3-319-97909-0_40 restricted
spellingShingle Xue, M.
Duan, X.
Liu, Wan-Quan
Wang, Y.
An ICA-based other-race effect elimination for facial expression recognition
title An ICA-based other-race effect elimination for facial expression recognition
title_full An ICA-based other-race effect elimination for facial expression recognition
title_fullStr An ICA-based other-race effect elimination for facial expression recognition
title_full_unstemmed An ICA-based other-race effect elimination for facial expression recognition
title_short An ICA-based other-race effect elimination for facial expression recognition
title_sort ica-based other-race effect elimination for facial expression recognition
url http://hdl.handle.net/20.500.11937/71336