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
본 논문에서는 특징분포의 상호투영을 기반으로 한 새로운 얼굴인식 방법을 제안한다. 제안된 방법은 두 가지 특징 분포 사이에 새로운 강력한 측정을 도입합니다. 이 측정값은 각 평균값을 반대 특성 분포에 투영하여 얻은 두 거리 값의 조화 평균으로 계산됩니다. 제안된 방법은 두 부분공간의 고유값 분석을 필요로 하지 않습니다. 이 방법은 시간적 영상 시퀀스의 얼굴 인식 작업에 적용되었습니다. 실험 결과는 기존 방법에 비해 식별 성능 저하 없이 계산 비용이 향상되었음을 보여줍니다.
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부
Akira INOUE, Atsushi SATO, "Face Recognition Based on Mutual Projection of Feature Distributions" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 7, pp. 1878-1884, July 2008, doi: 10.1093/ietisy/e91-d.7.1878.
Abstract: This paper proposes a new face recognition method based on mutual projection of feature distributions. The proposed method introduces a new robust measurement between two feature distributions. This measurement is computed by a harmonic mean of two distance values obtained by projection of each mean value into the opposite feature distribution. The proposed method does not require eigenvalue analysis of the two subspaces. This method was applied to face recognition task of temporal image sequence. Experimental results demonstrate that the computational cost was improved without degradation of identification performance in comparison with the conventional method.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.7.1878/_p
부
@ARTICLE{e91-d_7_1878,
author={Akira INOUE, Atsushi SATO, },
journal={IEICE TRANSACTIONS on Information},
title={Face Recognition Based on Mutual Projection of Feature Distributions},
year={2008},
volume={E91-D},
number={7},
pages={1878-1884},
abstract={This paper proposes a new face recognition method based on mutual projection of feature distributions. The proposed method introduces a new robust measurement between two feature distributions. This measurement is computed by a harmonic mean of two distance values obtained by projection of each mean value into the opposite feature distribution. The proposed method does not require eigenvalue analysis of the two subspaces. This method was applied to face recognition task of temporal image sequence. Experimental results demonstrate that the computational cost was improved without degradation of identification performance in comparison with the conventional method.},
keywords={},
doi={10.1093/ietisy/e91-d.7.1878},
ISSN={1745-1361},
month={July},}
부
TY - JOUR
TI - Face Recognition Based on Mutual Projection of Feature Distributions
T2 - IEICE TRANSACTIONS on Information
SP - 1878
EP - 1884
AU - Akira INOUE
AU - Atsushi SATO
PY - 2008
DO - 10.1093/ietisy/e91-d.7.1878
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2008
AB - This paper proposes a new face recognition method based on mutual projection of feature distributions. The proposed method introduces a new robust measurement between two feature distributions. This measurement is computed by a harmonic mean of two distance values obtained by projection of each mean value into the opposite feature distribution. The proposed method does not require eigenvalue analysis of the two subspaces. This method was applied to face recognition task of temporal image sequence. Experimental results demonstrate that the computational cost was improved without degradation of identification performance in comparison with the conventional method.
ER -