Multimodal Feature-Level Fusion for Biometrics Identification System on IoMT Platform

• Main Authors: Yang Xin, Lingshuang Kong, Zhi Liu, Chunhua Wang, Hongliang Zhu, Mingcheng Gao, Chensu Zhao, Xiaoke Xu
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Multi-model fusion; fisher vector; liveness detection; personal identification; IoMT
• Online Access: https://ieeexplore.ieee.org/document/8315011/

Biometric systems have been actively emerging in various industries in the past few years and continue to provide higher-security features for access control systems. Many types of unimodal biometric systems have been developed. However, these systems are only capable of providing lowto mid-range security features. Thus, for higher-security features, the combination of two or more unimodal biometrics (multiple modalities) is required. In this paper, we propose a multimodal biometric system for person recognition using face, fingerprint, and finger vein images. Addressing this problem, we propose an efficient matching algorithm that is based on secondary calculation of the Fisher vector and uses three biometric modalities: face, fingerprint, and finger vein. The three modalities are combined and fusion is performed at the feature level. Furthermore, based on the method of feature fusion, the paper studies the fake feature which appears in the practical scene. The liveness detection is append to the system, detect the picture is real or fake based on DCT, then remove the fake picture to reduce the influence of accuracy rate, and increase the robust of system. The experimental results showed that the designed framework can achieve an excellent recognition rate and provide higher security than a unimodal biometric-based system, which are very important for a IoMT platform.