AuthFormer: Adaptive Multimodal biometric authentication transformer for middle-aged and elderly people

TL;DR

AuthFormer is an adaptive multimodal biometric authentication model designed for elderly users, utilizing a transformer architecture with cross-attention and GRN to improve accuracy and flexibility in biometric verification.

Contribution

The paper introduces AuthFormer, a novel adaptive multimodal biometric authentication model with a lightweight encoder, tailored for elderly users and trained on a specialized biometric database.

Findings

Achieves 99.73% accuracy in biometric authentication.

Uses only two encoder layers for optimal performance, reducing complexity.

Demonstrates improved adaptability to physiological variations in elderly users.

Abstract

Multimodal biometric authentication methods address the limitations of unimodal biometric technologies in security, robustness, and user adaptability. However, most existing methods depend on fixed combinations and numbers of biometric modalities, which restricts flexibility and adaptability in real-world applications. To overcome these challenges, we propose an adaptive multimodal biometric authentication model, AuthFormer, tailored for elderly users. AuthFormer is trained on the LUTBIO multimodal biometric database, containing biometric data from elderly individuals. By incorporating a cross-attention mechanism and a Gated Residual Network (GRN), the model improves adaptability to physiological variations in elderly users. Experiments show that AuthFormer achieves an accuracy of 99.73%. Additionally, its encoder requires only two layers to perform optimally, reducing complexity compared to traditional Transformer-based models.