Attention Map Is All We Need for Lightweight Fingerprint Liveness Detection

Abstract

To counter the security threats posed by presentation attacks on fingerprint authentication systems, various deep learning-based fingerprint liveness detection methods have been proposed. However, existing methods typically require significant computing resources or lengthy detection times to achieve high accuracy, which can limit their use in resource-constrained environments such as on-device applications. In this paper, we propose a novel fingerprint liveness detection method that utilizes a Multi-head Self-Attention mechanism. By focusing on important regions of fingerprint images, our proposed method maximizes detection accuracy while simultaneously minimizing both model size and detection time. Our evaluation on real-world datasets demonstrates that our proposed method achieves detection accuracy comparable to state-of-the-art methods while requiring the smallest model size and the least detection time, confirming that our proposed method is the most efficient liveness detection method available.

Keywords

• Fingerprint liveness detection
• lightweight method
• multi-head self-attention
• fingerprint authentication
• presentation attacks