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

TLDR

Biometric systems have emerged across industries, offering higher‑security access control, yet unimodal systems only provide low‑ to mid‑range security. The study proposes a multimodal biometric system combining face, fingerprint, and finger‑vein images, employing a Fisher‑vector–based matching algorithm and addressing fake‑feature detection to enhance security. The system fuses face, fingerprint, and finger‑vein features at the feature level, applies a Fisher‑vector–based matching algorithm, and incorporates DCT‑based liveness detection to filter fake images. Experiments demonstrate that the framework achieves high recognition accuracy and superior security compared to unimodal biometrics, making it valuable for IoMT platforms.

Abstract

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 low- to 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.