IEICE TRANSACTIONS on Information
Fusion of Multiple Facial Features for Age Estimation
Summary :
A novel age estimation method is presented which improves performance by fusing complementary information acquired from global and local features of the face. Two-directional two-dimensional principal component analysis ((2D)2PCA) is used for dimensionality reduction and construction of individual feature spaces. Each feature space contributes a confidence value which is calculated by Support vector machines (SVMs). The confidence values of all the facial features are then fused for final age estimation. Experimental results demonstrate that fusing multiple facial features can achieve significant accuracy gains over any single feature. Finally, we propose a fusion method that further improves accuracy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.9 pp.1815-1818
- Publication Date
- 2009/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.1815
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Li LU, Pengfei SHI, "Fusion of Multiple Facial Features for Age Estimation" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 9, pp. 1815-1818, September 2009, doi: 10.1587/transinf.E92.D.1815.
Abstract: A novel age estimation method is presented which improves performance by fusing complementary information acquired from global and local features of the face. Two-directional two-dimensional principal component analysis ((2D)2PCA) is used for dimensionality reduction and construction of individual feature spaces. Each feature space contributes a confidence value which is calculated by Support vector machines (SVMs). The confidence values of all the facial features are then fused for final age estimation. Experimental results demonstrate that fusing multiple facial features can achieve significant accuracy gains over any single feature. Finally, we propose a fusion method that further improves accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1815/_p
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@ARTICLE{e92-d_9_1815,
author={Li LU, Pengfei SHI, },
journal={IEICE TRANSACTIONS on Information},
title={Fusion of Multiple Facial Features for Age Estimation},
year={2009},
volume={E92-D},
number={9},
pages={1815-1818},
abstract={A novel age estimation method is presented which improves performance by fusing complementary information acquired from global and local features of the face. Two-directional two-dimensional principal component analysis ((2D)2PCA) is used for dimensionality reduction and construction of individual feature spaces. Each feature space contributes a confidence value which is calculated by Support vector machines (SVMs). The confidence values of all the facial features are then fused for final age estimation. Experimental results demonstrate that fusing multiple facial features can achieve significant accuracy gains over any single feature. Finally, we propose a fusion method that further improves accuracy.},
keywords={},
doi={10.1587/transinf.E92.D.1815},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Fusion of Multiple Facial Features for Age Estimation
T2 - IEICE TRANSACTIONS on Information
SP - 1815
EP - 1818
AU - Li LU
AU - Pengfei SHI
PY - 2009
DO - 10.1587/transinf.E92.D.1815
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2009
AB - A novel age estimation method is presented which improves performance by fusing complementary information acquired from global and local features of the face. Two-directional two-dimensional principal component analysis ((2D)2PCA) is used for dimensionality reduction and construction of individual feature spaces. Each feature space contributes a confidence value which is calculated by Support vector machines (SVMs). The confidence values of all the facial features are then fused for final age estimation. Experimental results demonstrate that fusing multiple facial features can achieve significant accuracy gains over any single feature. Finally, we propose a fusion method that further improves accuracy.
ER -
English
