IEICE TRANSACTIONS on Information
Multilinear Supervised Neighborhood Embedding with Local Descriptor Tensor for Face Recognition
Summary :
Subspace learning based face recognition methods have attracted considerable interest in recent years, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and some extensions for 2D analysis. However, a disadvantage of all these approaches is that they perform subspace analysis directly on the reshaped vector or matrix of pixel-level intensity, which is usually unstable under illumination or pose variance. In this paper, we propose to represent a face image as a local descriptor tensor, which is a combination of the descriptor of local regions (K*K-pixel patch) in the image, and is more efficient than the popular Bag-Of-Feature (BOF) model for local descriptor combination. Furthermore, we propose to use a multilinear subspace learning algorithm (Supervised Neighborhood Embedding-SNE) for discriminant feature extraction from the local descriptor tensor of face images, which can preserve local sample structure in feature space. We validate our proposed algorithm on Benchmark database Yale and PIE, and experimental results show recognition rate with our method can be greatly improved compared conventional subspace analysis methods especially for small training sample number.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.1 pp.158-161
- Publication Date
- 2011/01/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.158
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Xian-Hua HAN, Xu QIAO, Yen-Wei CHEN, "Multilinear Supervised Neighborhood Embedding with Local Descriptor Tensor for Face Recognition" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 1, pp. 158-161, January 2011, doi: 10.1587/transinf.E94.D.158.
Abstract: Subspace learning based face recognition methods have attracted considerable interest in recent years, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and some extensions for 2D analysis. However, a disadvantage of all these approaches is that they perform subspace analysis directly on the reshaped vector or matrix of pixel-level intensity, which is usually unstable under illumination or pose variance. In this paper, we propose to represent a face image as a local descriptor tensor, which is a combination of the descriptor of local regions (K*K-pixel patch) in the image, and is more efficient than the popular Bag-Of-Feature (BOF) model for local descriptor combination. Furthermore, we propose to use a multilinear subspace learning algorithm (Supervised Neighborhood Embedding-SNE) for discriminant feature extraction from the local descriptor tensor of face images, which can preserve local sample structure in feature space. We validate our proposed algorithm on Benchmark database Yale and PIE, and experimental results show recognition rate with our method can be greatly improved compared conventional subspace analysis methods especially for small training sample number.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.158/_p
Copy
@ARTICLE{e94-d_1_158,
author={Xian-Hua HAN, Xu QIAO, Yen-Wei CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Multilinear Supervised Neighborhood Embedding with Local Descriptor Tensor for Face Recognition},
year={2011},
volume={E94-D},
number={1},
pages={158-161},
abstract={Subspace learning based face recognition methods have attracted considerable interest in recent years, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and some extensions for 2D analysis. However, a disadvantage of all these approaches is that they perform subspace analysis directly on the reshaped vector or matrix of pixel-level intensity, which is usually unstable under illumination or pose variance. In this paper, we propose to represent a face image as a local descriptor tensor, which is a combination of the descriptor of local regions (K*K-pixel patch) in the image, and is more efficient than the popular Bag-Of-Feature (BOF) model for local descriptor combination. Furthermore, we propose to use a multilinear subspace learning algorithm (Supervised Neighborhood Embedding-SNE) for discriminant feature extraction from the local descriptor tensor of face images, which can preserve local sample structure in feature space. We validate our proposed algorithm on Benchmark database Yale and PIE, and experimental results show recognition rate with our method can be greatly improved compared conventional subspace analysis methods especially for small training sample number.},
keywords={},
doi={10.1587/transinf.E94.D.158},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Multilinear Supervised Neighborhood Embedding with Local Descriptor Tensor for Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 158
EP - 161
AU - Xian-Hua HAN
AU - Xu QIAO
AU - Yen-Wei CHEN
PY - 2011
DO - 10.1587/transinf.E94.D.158
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2011
AB - Subspace learning based face recognition methods have attracted considerable interest in recent years, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and some extensions for 2D analysis. However, a disadvantage of all these approaches is that they perform subspace analysis directly on the reshaped vector or matrix of pixel-level intensity, which is usually unstable under illumination or pose variance. In this paper, we propose to represent a face image as a local descriptor tensor, which is a combination of the descriptor of local regions (K*K-pixel patch) in the image, and is more efficient than the popular Bag-Of-Feature (BOF) model for local descriptor combination. Furthermore, we propose to use a multilinear subspace learning algorithm (Supervised Neighborhood Embedding-SNE) for discriminant feature extraction from the local descriptor tensor of face images, which can preserve local sample structure in feature space. We validate our proposed algorithm on Benchmark database Yale and PIE, and experimental results show recognition rate with our method can be greatly improved compared conventional subspace analysis methods especially for small training sample number.
ER -
English
