An Improved Face Clustering Method Using Weighted Graph for Matched SIFT Keypoints in Face Region
Summary :
In this paper, we propose an improved face clustering method using a weighted graph-based approach. We combine two parameters as the weight of a graph to improve clustering performance. One is average similarity, which is calculated with two constraints of geometric and symmetric properties, and the other is a newly proposed parameter called the orientation matching ratio, which is calculated from orientation analysis for matched keypoints in the face region. According to the results of face clustering for several datasets, the proposed method shows improved results compared to the previous method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.4 pp.967-971
- Publication Date
- 2013/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.967
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Keyword
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Ji-Soo KEUM, Hyon-Soo LEE, "An Improved Face Clustering Method Using Weighted Graph for Matched SIFT Keypoints in Face Region" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 4, pp. 967-971, April 2013, doi: 10.1587/transinf.E96.D.967.
Abstract: In this paper, we propose an improved face clustering method using a weighted graph-based approach. We combine two parameters as the weight of a graph to improve clustering performance. One is average similarity, which is calculated with two constraints of geometric and symmetric properties, and the other is a newly proposed parameter called the orientation matching ratio, which is calculated from orientation analysis for matched keypoints in the face region. According to the results of face clustering for several datasets, the proposed method shows improved results compared to the previous method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.967/_p
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@ARTICLE{e96-d_4_967,
author={Ji-Soo KEUM, Hyon-Soo LEE, },
journal={IEICE TRANSACTIONS on Information},
title={An Improved Face Clustering Method Using Weighted Graph for Matched SIFT Keypoints in Face Region},
year={2013},
volume={E96-D},
number={4},
pages={967-971},
abstract={In this paper, we propose an improved face clustering method using a weighted graph-based approach. We combine two parameters as the weight of a graph to improve clustering performance. One is average similarity, which is calculated with two constraints of geometric and symmetric properties, and the other is a newly proposed parameter called the orientation matching ratio, which is calculated from orientation analysis for matched keypoints in the face region. According to the results of face clustering for several datasets, the proposed method shows improved results compared to the previous method.},
keywords={},
doi={10.1587/transinf.E96.D.967},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - An Improved Face Clustering Method Using Weighted Graph for Matched SIFT Keypoints in Face Region
T2 - IEICE TRANSACTIONS on Information
SP - 967
EP - 971
AU - Ji-Soo KEUM
AU - Hyon-Soo LEE
PY - 2013
DO - 10.1587/transinf.E96.D.967
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2013
AB - In this paper, we propose an improved face clustering method using a weighted graph-based approach. We combine two parameters as the weight of a graph to improve clustering performance. One is average similarity, which is calculated with two constraints of geometric and symmetric properties, and the other is a newly proposed parameter called the orientation matching ratio, which is calculated from orientation analysis for matched keypoints in the face region. According to the results of face clustering for several datasets, the proposed method shows improved results compared to the previous method.
ER -
English
