Hypercomplex extreme learning machine with its application in multispectral palmprint recognition

Longbin Lu; Xinman Zhang; Xuebin Xu

doi:10.1371/journal.pone.0209083

Hypercomplex extreme learning machine with its application in multispectral palmprint recognition

PLoS One. 2019 Apr 15;14(4):e0209083. doi: 10.1371/journal.pone.0209083. eCollection 2019.

Authors

Longbin Lu¹, Xinman Zhang¹, Xuebin Xu²

Affiliations

¹ MOE Key Lab for Intelligent Networks and Network Security, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
² Guangdong Xi'an Jiaotong University Academy, Foshan, Guangdong, China.

Abstract

An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest due to their fast learning speed, satisfactory generalization ability and ease of implementation. In this paper, we extend this theory to hypercomplex space and attempt to simultaneously consider multisource information using a hypercomplex representation. To illustrate the performance of the proposed hypercomplex extreme learning machine (HELM), we have applied this scheme to the task of multispectral palmprint recognition. Images from different spectral bands are utilized to construct the hypercomplex space. Extensive experiments conducted on the PolyU and CASIA multispectral databases demonstrate that the HELM scheme can achieve competitive results. The source code together with datasets involved in this paper can be available for free download at https://figshare.com/s/01aef7d48840afab9d6d.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Algorithms
Dermatoglyphics*
Female
Humans
Machine Learning*
Male
Middle Aged
Neural Networks, Computer
Pattern Recognition, Automated / methods
Young Adult

Grants and funding

This work is supported by the National Natural Science Foundation of China (No. 61673316; http://www.nsfc.gov.cn/), and the special grants from the Major Science and Technology Foundation of Guangdong Province (No. 2015B010104002; http://zdkjzx.gdstc.gov.cn/). XMZ is the receiver of all the mentioned funds. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.