A novel facial expression recognition based on the curevlet features

Curvelet transform has been recently proved to be a powerful tool for multi-resolution analysis on images. In this paper we propose a new approach for facial expression recognition based on features extracted via curvelet transform. First curvelet transform is presented and its advantages in image a...

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Bibliographic Details
Main Authors: Zhou, Juxiang, Wang, Yun-qiong, Xu, Tianwei, Liu, Wan-quan
Other Authors: K.L. Chan
Format: Conference Paper
Published: IEEE Computer Society 2010
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/23208
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author Zhou, Juxiang
Wang, Yun-qiong
Xu, Tianwei
Liu, Wan-quan
author2 K.L. Chan
author_facet K.L. Chan
Zhou, Juxiang
Wang, Yun-qiong
Xu, Tianwei
Liu, Wan-quan
author_sort Zhou, Juxiang
building Curtin Institutional Repository
collection Online Access
description Curvelet transform has been recently proved to be a powerful tool for multi-resolution analysis on images. In this paper we propose a new approach for facial expression recognition based on features extracted via curvelet transform. First curvelet transform is presented and its advantages in image analysis are described. Then the coefficients of curvelet in selected scales and angles are used as features for image analysis. Consequently the Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are used to reduce and optimize the curvelet features. Finally we use the nearest neighbor classifier to recognize the facial expressions based on these features. The experimental results on JAFFE and Cohn Kanade two benchmark databases show that the proposed approach outperforms the PCA and LDA techniques on the original image pixel values as well as its counterparts with the wavelet features.
first_indexed 2025-11-14T07:47:08Z
format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:47:08Z
publishDate 2010
publisher IEEE Computer Society
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-232082023-01-13T07:56:31Z A novel facial expression recognition based on the curevlet features Zhou, Juxiang Wang, Yun-qiong Xu, Tianwei Liu, Wan-quan K.L. Chan A. Sugimoto H. Lu Wavelet Facial expression recognition Curvelet transform LDA PCA Curvelet transform has been recently proved to be a powerful tool for multi-resolution analysis on images. In this paper we propose a new approach for facial expression recognition based on features extracted via curvelet transform. First curvelet transform is presented and its advantages in image analysis are described. Then the coefficients of curvelet in selected scales and angles are used as features for image analysis. Consequently the Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are used to reduce and optimize the curvelet features. Finally we use the nearest neighbor classifier to recognize the facial expressions based on these features. The experimental results on JAFFE and Cohn Kanade two benchmark databases show that the proposed approach outperforms the PCA and LDA techniques on the original image pixel values as well as its counterparts with the wavelet features. 2010 Conference Paper http://hdl.handle.net/20.500.11937/23208 IEEE Computer Society fulltext
spellingShingle Wavelet
Facial expression recognition
Curvelet transform
LDA
PCA
Zhou, Juxiang
Wang, Yun-qiong
Xu, Tianwei
Liu, Wan-quan
A novel facial expression recognition based on the curevlet features
title A novel facial expression recognition based on the curevlet features
title_full A novel facial expression recognition based on the curevlet features
title_fullStr A novel facial expression recognition based on the curevlet features
title_full_unstemmed A novel facial expression recognition based on the curevlet features
title_short A novel facial expression recognition based on the curevlet features
title_sort novel facial expression recognition based on the curevlet features
topic Wavelet
Facial expression recognition
Curvelet transform
LDA
PCA
url http://hdl.handle.net/20.500.11937/23208