AuthFormer: Adaptive Multimodal biometric authentication transformer for middle-aged and elderly people
This addresses authentication challenges for elderly people, but it is incremental as it builds on existing multimodal and Transformer methods.
The paper tackled the problem of inflexible multimodal biometric authentication for elderly users by proposing AuthFormer, which achieved 99.73% accuracy and reduced model complexity with a two-layer encoder.
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.