Weighted Voting of Discriminative Regions for Face Recognition
Summary :
This paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights to regions for weighted voting. We construct a decision dictionary according to the recognition results of selected regions in the training phase, and this dictionary is used in a self-defined loss function to obtain weights. The final identity of test sample is the weighted voting of selected regions. In this paper, we combine the WVDR strategy with CRC and SRC separately, and extensive experiments show that our method outperforms the baseline and some representative algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.11 pp.2734-2737
- Publication Date
- 2017/11/01
- Publicized
- 2017/08/04
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDL8124
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Wenming YANG
Tsinghua University
Riqiang GAO
Tsinghua University
Qingmin LIAO
Tsinghua University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Wenming YANG, Riqiang GAO, Qingmin LIAO, "Weighted Voting of Discriminative Regions for Face Recognition" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 11, pp. 2734-2737, November 2017, doi: 10.1587/transinf.2017EDL8124.
Abstract: This paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights to regions for weighted voting. We construct a decision dictionary according to the recognition results of selected regions in the training phase, and this dictionary is used in a self-defined loss function to obtain weights. The final identity of test sample is the weighted voting of selected regions. In this paper, we combine the WVDR strategy with CRC and SRC separately, and extensive experiments show that our method outperforms the baseline and some representative algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8124/_p
Copy
@ARTICLE{e100-d_11_2734,
author={Wenming YANG, Riqiang GAO, Qingmin LIAO, },
journal={IEICE TRANSACTIONS on Information},
title={Weighted Voting of Discriminative Regions for Face Recognition},
year={2017},
volume={E100-D},
number={11},
pages={2734-2737},
abstract={This paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights to regions for weighted voting. We construct a decision dictionary according to the recognition results of selected regions in the training phase, and this dictionary is used in a self-defined loss function to obtain weights. The final identity of test sample is the weighted voting of selected regions. In this paper, we combine the WVDR strategy with CRC and SRC separately, and extensive experiments show that our method outperforms the baseline and some representative algorithms.},
keywords={},
doi={10.1587/transinf.2017EDL8124},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Weighted Voting of Discriminative Regions for Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 2734
EP - 2737
AU - Wenming YANG
AU - Riqiang GAO
AU - Qingmin LIAO
PY - 2017
DO - 10.1587/transinf.2017EDL8124
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2017
AB - This paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights to regions for weighted voting. We construct a decision dictionary according to the recognition results of selected regions in the training phase, and this dictionary is used in a self-defined loss function to obtain weights. The final identity of test sample is the weighted voting of selected regions. In this paper, we combine the WVDR strategy with CRC and SRC separately, and extensive experiments show that our method outperforms the baseline and some representative algorithms.
ER -
English
