IEICE TRANSACTIONS on Fundamentals
Facial Expression Recognition Based on Sparse Locality Preserving Projection
Summary :
In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.7 pp.1650-1653
- Publication Date
- 2014/07/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E97.A.1650
- Type of Manuscript
- LETTER
- Category
- Image
Authors
Jingjie YAN
Southeast University
Wenming ZHENG
Southeast University
Minghai XIN
Southeast University
Jingwei YAN
Southeast University
Keyword
Latest Issue
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Jingjie YAN, Wenming ZHENG, Minghai XIN, Jingwei YAN, "Facial Expression Recognition Based on Sparse Locality Preserving Projection" in IEICE TRANSACTIONS on Fundamentals,
vol. E97-A, no. 7, pp. 1650-1653, July 2014, doi: 10.1587/transfun.E97.A.1650.
Abstract: In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.1650/_p
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@ARTICLE{e97-a_7_1650,
author={Jingjie YAN, Wenming ZHENG, Minghai XIN, Jingwei YAN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Facial Expression Recognition Based on Sparse Locality Preserving Projection},
year={2014},
volume={E97-A},
number={7},
pages={1650-1653},
abstract={In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.},
keywords={},
doi={10.1587/transfun.E97.A.1650},
ISSN={1745-1337},
month={July},}
Copy
TY - JOUR
TI - Facial Expression Recognition Based on Sparse Locality Preserving Projection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1650
EP - 1653
AU - Jingjie YAN
AU - Wenming ZHENG
AU - Minghai XIN
AU - Jingwei YAN
PY - 2014
DO - 10.1587/transfun.E97.A.1650
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2014
AB - In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.
ER -
English
