A secure fingerprint recognition system must contain both a presentation
attack (i.e., spoof) detection and recognition module in order to protect users
against unwanted access by malicious users. Traditionally, these tasks would be
carried out by two independent systems; however, recent studies have
demonstrated the potential to have one unified system architecture in order to
reduce the computational burdens on the system, while maintaining high
accuracy. In this work, we leverage a vision transformer architecture for joint
spoof detection and matching and report competitive results with
state-of-the-art (SOTA) models for both a sequential system (two ViT models
operating independently) and a unified architecture (a single ViT model for
both tasks). ViT models are particularly well suited for this task as the ViT's
global embedding encodes features useful for recognition, whereas the
individual, local embeddings are useful for spoof detection. We demonstrate the
capability of our unified model to achieve an average integrated matching (IM)
accuracy of 98.87% across LivDet 2013 and 2015 CrossMatch sensors. This is
comparable to IM accuracy of 98.95% of our sequential dual-ViT system, but with
~50% of the parameters and ~58% of the latency.

本研究提出使用 Vision Transformer（ViT）模型的统一系统架构，实现虚假检测和匹配，并取得了与最先进（SOTA）的顺序系统相当的 98.87% 的平均集成匹配（IM）准确率，但参数只有后者的约 50％，延迟只有后者的约 58％。该方法适用于安全指纹识别等场景。