Multimodal Approach for Enhancing Biometric Authentication

Abstract

Unimodal biometric systems rely on a single source or unique individual biological trait for measurement and examination. Fingerprint-based biometric systems are the most common, but they are vulnerable to presentation attacks or spoofing when a fake fingerprint is presented to the sensor. To address this issue, we propose an enhanced biometric system based on a multimodal approach using two types of biological traits. We propose to combine fingerprint and Electrocardiogram (ECG) signals to mitigate spoofing attacks. Specifically, we design a multimodal deep learning architecture that accepts fingerprints and ECG as inputs and fuses the feature vectors using stacking and channel-wise approaches. The feature extraction backbone of the architecture is based on data-efficient transformers. The experimental results demonstrate the promising capabilities of the proposed approach in enhancing the robustness of the system to presentation attacks.

CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords

“…The CNN had 17 layers and classified 66 data samples with 99.8% accuracy. Ammour et al [39] proposed a biometric authentication system utilizing Resnet50 for learning using two factors: ECG signals and fingerprint images. A multi-modal system was implemented using signal and image data as factors, and data samples from 70 people were classified with an accuracy of 98.6%.…”

“…Employing the sequential authentication system, they first confirmed the Table 2 presents a list of recent CNN-based MFA systems which have obtained more than 97% accuracy. Ahamed et al [38] and Ammour et al [39] suggested systems with 17 layers and 50 layers, respectively, which are not suitable for use in mobile devices. Sing and Tiwari [40] proposed a relatively lightweight system with seven layers, but did not consider the safety of the data.…”