TY - GEN
T1 - A Comparative Study on Canonical Correlation Analysis-Based Multi-feature Fusion for Palmprint Recognition
AU - Wu, Yihang
AU - Hu, Junlin
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Contactless palmprint recognition provides high-accuracy and friendly experience for users without directly contacting the recognition device. Currently, many existing methods have shown relatively satisfying performance, but there are still several problems such as the limited patterns extracted by single feature extraction approach and the huge gap between hand-crafted feature-based approaches and deep learning feature-based approaches. To this end, in this paper, we make use of multiple palmprint features and exploit the benefits of hand-crafted features and deep features in a unified framework using Canonical Correlation Analysis (CCA) method, and present a comparative study on CCA-based multi-feature fusion for palmprint recognition. In the experiments, the best feature fusion scheme achieves 100% accuracy on Tongji palmprint dataset and shows good generalization ability on IITD and CASIA palmprint datasets. Extensive comparative experiments of different approaches on three palmprint datasets demonstrate the effectiveness of CCA-based multi-feature fusion method and the prospects of applying feature fusion techniques in palmprint recognition.
AB - Contactless palmprint recognition provides high-accuracy and friendly experience for users without directly contacting the recognition device. Currently, many existing methods have shown relatively satisfying performance, but there are still several problems such as the limited patterns extracted by single feature extraction approach and the huge gap between hand-crafted feature-based approaches and deep learning feature-based approaches. To this end, in this paper, we make use of multiple palmprint features and exploit the benefits of hand-crafted features and deep features in a unified framework using Canonical Correlation Analysis (CCA) method, and present a comparative study on CCA-based multi-feature fusion for palmprint recognition. In the experiments, the best feature fusion scheme achieves 100% accuracy on Tongji palmprint dataset and shows good generalization ability on IITD and CASIA palmprint datasets. Extensive comparative experiments of different approaches on three palmprint datasets demonstrate the effectiveness of CCA-based multi-feature fusion method and the prospects of applying feature fusion techniques in palmprint recognition.
KW - Canonical Correlation Analysis
KW - Palmprint recognition
KW - deep feature
KW - multi-feature fusion
UR - https://www.scopus.com/pages/publications/85180554978
U2 - 10.1007/978-981-99-8565-4_5
DO - 10.1007/978-981-99-8565-4_5
M3 - 会议稿件
AN - SCOPUS:85180554978
SN - 9789819985647
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 46
EP - 54
BT - Biometric Recognition - 17th Chinese Conference, CCBR 2023, Proceedings
A2 - Jia, Wei
A2 - Kang, Wenxiong
A2 - Pan, Zaiyu
A2 - Bian, Zhengfu
A2 - Wang, Jun
A2 - Ben, Xianye
A2 - Yu, Shiqi
A2 - He, Zhaofeng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th Chinese Conference on Biometric Recognition, CCBR 2023
Y2 - 1 December 2023 through 3 December 2023
ER -