The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kaedah pengecaman muka berasaskan pembelajaran subruang telah menarik minat yang besar dalam beberapa tahun kebelakangan ini, termasuk Analisis Komponen Utama (PCA), Analisis Komponen Bebas (ICA), Analisis Diskriminasi Linear (LDA) dan beberapa sambungan untuk analisis 2D. Walau bagaimanapun, kelemahan semua pendekatan ini ialah ia melakukan analisis subruang secara langsung pada vektor atau matriks keamatan tahap piksel yang dibentuk semula, yang biasanya tidak stabil di bawah pencahayaan atau varians pose. Dalam makalah ini, kami mencadangkan untuk mewakili imej muka sebagai tensor deskriptor tempatan, yang merupakan gabungan deskriptor kawasan setempat (tampalan piksel K*K) dalam imej, dan lebih cekap daripada Bag-Of- yang popular. Model ciri (BOF) untuk gabungan deskriptor tempatan. Tambahan pula, kami mencadangkan untuk menggunakan algoritma pembelajaran subruang berbilang linear (Supervised Neighborhood Embedding-SNE) untuk pengekstrakan ciri diskriminasi daripada tensor deskriptor tempatan bagi imej muka, yang boleh mengekalkan struktur sampel tempatan dalam ruang ciri. Kami mengesahkan algoritma cadangan kami pada pangkalan data Penanda Aras Yale dan PIE, dan keputusan percubaan menunjukkan kadar pengecaman dengan kaedah kami boleh dipertingkatkan dengan banyak berbanding kaedah analisis subruang konvensional terutamanya untuk bilangan sampel latihan yang kecil.
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Salinan
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
Salinan
@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},}
Salinan
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 -