Fuzzy Brain Storm Optimization and Adaptive Thresholding for Multimodal Vein-Based Recognition System

Abstract

Nowadays, conventional security method of using passwords can be easily forged by unauthorized person. Hence, biometric cues such as fingerprints, voice, palm print, and face are more preferable for recognition but to preserve the liveliness, another one important biometric trait is vein pattern, which is formed by the subcutaneous blood vessels that contain all the achievable recognition properties. Accordingly, in this paper, we propose a multibiometric system using palm vein, hand vein, and finger vein. Here, Holoentropy-based thresholding mechanism is newly developed for extracting the vein patterns. Also, Fuzzy Brain Storm Optimization (FBSO) method is proposed for score level fusion to achieve the better recognition performance. These two contributions are effectively included in the biometric recognition system and the performance analysis of the proposed method is carried out using the benchmark datasets of palm vein image, finger vein image, and hand vein image. The quantitative results are analyzed with the help of FAR, FRR, and accuracy. From outcome, we proved that the proposed FBSO approach attained a higher accuracy of 81.3% than the existing methods.