Translator Disclaimer
11 February 2020 Effective deep ensemble hashing for open-set palmprint recognition
Author Affiliations +
Abstract

Recently, palmprint recognition has made huge progress and attracted the attention of more and more researchers. However, current research rarely involves open-set palmprint recognition. We proposed deep ensemble hashing (DEH) for open-set palmprint recognition. Based on the online gradient boosting model, we trained multiple learners in DEH, which focus on identifying different samples. In order to increase the diversity between learners, activation loss and adversarial loss were introduced. Through minimizing activation loss, the neurons of different learners restrained each other, and through adversarial loss, the optimal distance between the features extracted by different learners was obtained. Palmprint identification and verification experiments were performed on PolyU multispectral database and our self-built databases. The results show the effectiveness of DEH in deal with open-set palmprint recognition. Compared to baseline models, DEH increased the recognition accuracy by up to 6.67% and reduced the equal error rate by up to 3.48%.

© 2020 SPIE and IS&T 1017-9909/2020/$28.00 © 2020 SPIE and IS&T
Huikai Shao, Dexing Zhong, and Xuefeng Du "Effective deep ensemble hashing for open-set palmprint recognition," Journal of Electronic Imaging 29(1), 013018 (11 February 2020). https://doi.org/10.1117/1.JEI.29.1.013018
Received: 1 July 2019; Accepted: 22 January 2020; Published: 11 February 2020
JOURNAL ARTICLE
18 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Sampling design for face recognition
Proceedings of SPIE (April 17 2006)
A hybrid approach for face template protection
Proceedings of SPIE (March 17 2008)
Local SIFT analysis for hand vein pattern verification
Proceedings of SPIE (November 18 2009)
Evaluation of decision forests on text categorization
Proceedings of SPIE (December 22 1999)

Back to Top