Multimodal biometrics Fusion based on TER and Hybrid Intelligent Multiple Hidden Layer Probabilistic Extreme Learning Machine

DOI

10.2991/ijcis.11.1.71How to use a DOI?

Keywords

MultiBiometrics; Total Error Rate(TER); Extreme Learning Machine(ELM); Differential Evolution(DE); Particle Swarm Optimization(PSO)

Abstract

In this paper, a novel fusion method based on Total Error Rate (TER) and multiple hidden layer probabilistic extreme learning machine is proposed. At first, the study transfers the matching scores into TER based on corresponding False Reject Rates (FRR) and False Accept Rates (FAR) aims at avoiding to calculating the posterior probability. At the second, a new fusion strategy based on multiple hidden layer probabilistic extreme learning machine is introduced, which optimizes the architecture of hidden nodes by weighted calculation of different output matrices and then transforms the numeric output of ELM to the probabilistic outputs and unifies the outputs in a fixed range, the matrices weights and the output weights are optimized using a hybrid intelligent algorithm based on differential evolution and particle swarm optimization. Experiment result shown that the proposed method renders very good performance as it is quite computationally and outperforms the traditional score level fusion schemes, the experimental result also confirms the effectiveness of the proposed method to improve the performance of multibiometric system.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Di Wu
AU  - Qin Wan
PY  - 2018
DA  - 2018/05/02
TI  - Multimodal biometrics Fusion based on TER and Hybrid Intelligent Multiple Hidden Layer Probabilistic Extreme Learning Machine
JO  - International Journal of Computational Intelligence Systems
SP  - 936
EP  - 950
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.71
DO  - 10.2991/ijcis.11.1.71
ID  - Wu2018
ER  -