TY - GEN
T1 - Improving feature based dorsal hand vein recognition through Random Keypoint Generation and fine-grained matching
AU - Zhang, Renke
AU - Huang, Di
AU - Wang, Yiding
AU - Wang, Yunhong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/29
Y1 - 2015/6/29
N2 - Recently, SIFT-like approaches have shown their advantages of performance and robustness in dorsal hand vein recognition. This paper presents a novel method to recognize the vein pattern of the dorsal hand, which discusses two important issues in the SIFT-like framework, i.e. keypoint detection and matching. For the former, a Gaussian Distribution based Random Keypoint Generation method (GDRKG) is proposed to localize a sufficient set of distinctive keypoints, which largely reduces the computational complexity of the state of the art ones, such as DoG, Harris, and Hessian. For the latter, a Multi-task Sparse Representation Classifier (MtSRC) based fine-grained matching strategy is introduced instead of traditional coarse-grained matching, to precisely measure the similarity between the feature sets of the samples. The proposed method is tested on a dataset of 2040 vein images of 204 dorsal hands, and it outperforms the state of the arts clearly proving its effectiveness.
AB - Recently, SIFT-like approaches have shown their advantages of performance and robustness in dorsal hand vein recognition. This paper presents a novel method to recognize the vein pattern of the dorsal hand, which discusses two important issues in the SIFT-like framework, i.e. keypoint detection and matching. For the former, a Gaussian Distribution based Random Keypoint Generation method (GDRKG) is proposed to localize a sufficient set of distinctive keypoints, which largely reduces the computational complexity of the state of the art ones, such as DoG, Harris, and Hessian. For the latter, a Multi-task Sparse Representation Classifier (MtSRC) based fine-grained matching strategy is introduced instead of traditional coarse-grained matching, to precisely measure the similarity between the feature sets of the samples. The proposed method is tested on a dataset of 2040 vein images of 204 dorsal hands, and it outperforms the state of the arts clearly proving its effectiveness.
UR - https://www.scopus.com/pages/publications/84943241910
U2 - 10.1109/ICB.2015.7139057
DO - 10.1109/ICB.2015.7139057
M3 - 会议稿件
AN - SCOPUS:84943241910
T3 - Proceedings of 2015 International Conference on Biometrics, ICB 2015
SP - 326
EP - 333
BT - Proceedings of 2015 International Conference on Biometrics, ICB 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IAPR International Conference on Biometrics, ICB 2015
Y2 - 19 May 2015 through 22 May 2015
ER -