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
부분 공간 학습 기반 얼굴 인식 방법은 PCA(주성분 분석), ICA(독립 구성 요소 분석), LDA(선형 판별 분석) 및 2D 분석을 위한 일부 확장을 포함하여 최근 몇 년 동안 상당한 관심을 불러일으켰습니다. 그러나 이러한 모든 접근 방식의 단점은 재구성된 벡터 또는 픽셀 수준 강도의 행렬에 대해 부분 공간 분석을 직접 수행한다는 것입니다. 이는 일반적으로 조명이나 포즈 변화에 따라 불안정합니다. 본 논문에서는 얼굴 이미지를 로컬 디스크립터 텐서(local descriptor tensor)로 표현하는 것을 제안합니다. 이는 이미지 내 로컬 영역의 디스크립터(K*K-픽셀 패치)를 조합한 것이며 널리 사용되는 Bag-Of-텐서보다 효율적입니다. 로컬 설명자 조합을 위한 기능(BOF) 모델입니다. 또한, 우리는 얼굴 이미지의 로컬 설명자 텐서로부터 판별적 특징 추출을 위해 다중 선형 부분 공간 학습 알고리즘(Supervised Neighborhood Embedding-SNE)을 사용하여 특징 공간에서 로컬 샘플 구조를 보존할 수 있도록 제안합니다. 우리는 벤치마크 데이터베이스 Yale 및 PIE에서 제안된 알고리즘을 검증했으며, 실험 결과는 특히 작은 훈련 샘플 수에 대해 기존의 부분 공간 분석 방법에 비해 우리 방법을 사용한 인식률이 크게 향상될 수 있음을 보여줍니다.
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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
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@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},}
부
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 -