Facial Expression Recognition via Sparse Representation

Ruicong ZHI; Qiuqi RUAN; Zhifei WANG

doi:10.1587/transinf.E95.D.2347

Facial Expression Recognition via Sparse Representation

Ruicong ZHI, Qiuqi RUAN, Zhifei WANG

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A facial components based facial expression recognition algorithm with sparse representation classifier is proposed. Sparse representation classifier is based on sparse representation and computed by L1-norm minimization problem on facial components. The features of “important” training samples are selected to represent test sample. Furthermore, fuzzy integral is utilized to fuse individual classifiers for facial components. Experiments for frontal views and partially occluded facial images show that this method is efficient and robust to partial occlusion on facial images.

Publication: IEICE TRANSACTIONS on Information Vol.E95-D No.9 pp.2347-2350

Publication Date: 2012/09/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E95.D.2347

Type of Manuscript: LETTER

Category: Pattern Recognition

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Ruicong ZHI, Qiuqi RUAN, Zhifei WANG, "Facial Expression Recognition via Sparse Representation" in IEICE TRANSACTIONS on Information, vol. E95-D, no. 9, pp. 2347-2350, September 2012, doi: 10.1587/transinf.E95.D.2347.
Abstract: A facial components based facial expression recognition algorithm with sparse representation classifier is proposed. Sparse representation classifier is based on sparse representation and computed by L1-norm minimization problem on facial components. The features of “important” training samples are selected to represent test sample. Furthermore, fuzzy integral is utilized to fuse individual classifiers for facial components. Experiments for frontal views and partially occluded facial images show that this method is efficient and robust to partial occlusion on facial images.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2347/_p

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@ARTICLE{e95-d_9_2347,
author={Ruicong ZHI, Qiuqi RUAN, Zhifei WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Facial Expression Recognition via Sparse Representation},
year={2012},
volume={E95-D},
number={9},
pages={2347-2350},
abstract={A facial components based facial expression recognition algorithm with sparse representation classifier is proposed. Sparse representation classifier is based on sparse representation and computed by L1-norm minimization problem on facial components. The features of “important” training samples are selected to represent test sample. Furthermore, fuzzy integral is utilized to fuse individual classifiers for facial components. Experiments for frontal views and partially occluded facial images show that this method is efficient and robust to partial occlusion on facial images.},
keywords={},
doi={10.1587/transinf.E95.D.2347},
ISSN={1745-1361},
month={September},}

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TY - JOUR
TI - Facial Expression Recognition via Sparse Representation
T2 - IEICE TRANSACTIONS on Information
SP - 2347
EP - 2350
AU - Ruicong ZHI
AU - Qiuqi RUAN
AU - Zhifei WANG
PY - 2012
DO - 10.1587/transinf.E95.D.2347
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2012
AB - A facial components based facial expression recognition algorithm with sparse representation classifier is proposed. Sparse representation classifier is based on sparse representation and computed by L1-norm minimization problem on facial components. The features of “important” training samples are selected to represent test sample. Furthermore, fuzzy integral is utilized to fuse individual classifiers for facial components. Experiments for frontal views and partially occluded facial images show that this method is efficient and robust to partial occlusion on facial images.
ER -