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
이 편지에서는 얼굴 인식을 위한 통계적 무상관 근접 클래스 판별(SUNCD) 접근 방식이 제안되었습니다. 이 접근법으로 얻은 최적의 판별 벡터는 특정 클래스 간 및 클래스 내 분산 행렬을 구성하고 Fisher 기준을 사용하여 하나의 클래스와 가까운 클래스, 즉 가장 가까운 이웃 클래스를 구별할 수 있습니다. 이러한 방식으로 SUNCD는 클래스별로 모든 판별 벡터 클래스를 획득합니다. 또한 SUNCD는 해당 클래스와 가장 인접한 클래스의 일부를 사용하여 모든 판별 벡터가 지역적으로 통계적인 비상관 제약 조건을 충족하도록 만듭니다. 공개 AR 얼굴 데이터베이스에 대한 실험은 제안된 접근 방식이 여러 대표적인 판별 방법보다 성능이 우수하다는 것을 보여줍니다.
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부
Sheng LI, Xiao-Yuan JING, Lu-Sha BIAN, Shi-Qiang GAO, Qian LIU, Yong-Fang YAO, "Facial Image Recognition Based on a Statistical Uncorrelated Near Class Discriminant Approach" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 4, pp. 934-937, April 2010, doi: 10.1587/transinf.E93.D.934.
Abstract: In this letter, a statistical uncorrelated near class discriminant (SUNCD) approach is proposed for face recognition. The optimal discriminant vector obtained by this approach can differentiate one class and its near classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices and using the Fisher criterion. In this manner, SUNCD acquires all discriminant vectors class by class. Furthermore, SUNCD makes every discriminant vector satisfy locally statistical uncorrelated constraints by using the corresponding class and part of its most neighboring classes. Experiments on the public AR face database demonstrate that the proposed approach outperforms several representative discriminant methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.934/_p
부
@ARTICLE{e93-d_4_934,
author={Sheng LI, Xiao-Yuan JING, Lu-Sha BIAN, Shi-Qiang GAO, Qian LIU, Yong-Fang YAO, },
journal={IEICE TRANSACTIONS on Information},
title={Facial Image Recognition Based on a Statistical Uncorrelated Near Class Discriminant Approach},
year={2010},
volume={E93-D},
number={4},
pages={934-937},
abstract={In this letter, a statistical uncorrelated near class discriminant (SUNCD) approach is proposed for face recognition. The optimal discriminant vector obtained by this approach can differentiate one class and its near classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices and using the Fisher criterion. In this manner, SUNCD acquires all discriminant vectors class by class. Furthermore, SUNCD makes every discriminant vector satisfy locally statistical uncorrelated constraints by using the corresponding class and part of its most neighboring classes. Experiments on the public AR face database demonstrate that the proposed approach outperforms several representative discriminant methods.},
keywords={},
doi={10.1587/transinf.E93.D.934},
ISSN={1745-1361},
month={April},}
부
TY - JOUR
TI - Facial Image Recognition Based on a Statistical Uncorrelated Near Class Discriminant Approach
T2 - IEICE TRANSACTIONS on Information
SP - 934
EP - 937
AU - Sheng LI
AU - Xiao-Yuan JING
AU - Lu-Sha BIAN
AU - Shi-Qiang GAO
AU - Qian LIU
AU - Yong-Fang YAO
PY - 2010
DO - 10.1587/transinf.E93.D.934
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2010
AB - In this letter, a statistical uncorrelated near class discriminant (SUNCD) approach is proposed for face recognition. The optimal discriminant vector obtained by this approach can differentiate one class and its near classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices and using the Fisher criterion. In this manner, SUNCD acquires all discriminant vectors class by class. Furthermore, SUNCD makes every discriminant vector satisfy locally statistical uncorrelated constraints by using the corresponding class and part of its most neighboring classes. Experiments on the public AR face database demonstrate that the proposed approach outperforms several representative discriminant methods.
ER -