ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection

• URL: http://arxiv.org/abs/2305.07602v1
• Date: Fri, 12 May 2023 16:51:14 GMT
• Title: ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection
• Authors: Steven A. Grosz, Kanishka P. Wijewardena, and Anil K. Jain
• Abstract summary: We leverage a vision transformer architecture for joint spoof detection and matching. We report competitive results with state-of-the-art (SOTA) models for both a sequential system and a unified architecture. 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.
• Score: 36.05807963935458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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