Learning Radial Basis Function Model with Matching Score Quality for Person Authentication in Multimodal Biometrics

Abstract

Recently multimodal biometrics technology that employs more than two types of biometrics data has been popularly used for person authentication and verification. In particular, the score-level fusion approach which combines matching scores from unimodal systems to make final decision has gained lots of attentions. In most of these works, however, they assume all the matching scores to be of the same quality. This assumption may cause the problem not to reflect such situation that the qualities of the matching scores from certain unimodal systems are relatively low. To deal with this problem, we propose the RBF based score-level fusion approach which incorporates the quality information of the scores in developing classification models. According to our experimental results, the proposed method using quality information showed its superiority in the performance of person authentication to the usual RBF based score-level fusion without using quality information.

Revisiting feature level and score level fusion techniques in multimodal biometrics system

“…The match score and quality score of an image were fused using support vector machine [27,35]. The RBF based score-level fusion approach incorporated the quality information of the scores in classification [28]. The score-level adaptation involved with the objective of rendering associated decision threshold to be dependent only on the class priors despite the changing acquisition conditions [29].…”

Multimodal Biometric Cryptosystem for Face and Ear Recognition Based on Fuzzy Vault

“…Multimodal biometrics has turn into an essential research tendency in enhancing biometric precision (Dahel and Xiao, 2003). Multimodal biometrics technology that uses more than two sorts of biometrics data has been universally applied for person authentication and verification (Choi and Shin, 2009).…”

Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

“…For example, various biometric characteristics might detected by distinct sensors and a dining practice of biometric procedures may be conducted to yield information gathering and additional unique communication methods may be intended to attain individual match scores [15]. Today, the innovation in multimodal biometrics using different classes of biometric information has become a charming methodology widely used with the ultimate goal of individual validation and control [16].…”