Vision Transformers for Fingerprint Presentation Attack Detection

Abstract

AbstractAutomated fingerprint recognition systems, while widely used, are still vulnerable to presentation attacks (PAs). The attacks can employ a wide range of presentation attack species (i.e., artifacts), varying from low-cost artifacts to sophisticated materials. A number of presentation attack detection (PAD) approaches have been specifically designed to detect and counteract presentation attacks on fingerprint systems. In this chapter, we study and analyze the well-employed Convolutional Neural Networks (CNN) with different architectures for fingerprint PAD by providing an extensive analysis of 23 different architectures in CNNs. In addition, this chapter presents a new approach introducing vision transformers for fingerprint PAD and validates it on two different public datasets, LivDet2015 and LivDet2019, used for fingerprint PAD. With the analysis of vision transformer-based F-PAD, this chapter covers both spectrum of CNNs and vision transformers to provide the reader with a one-place reference for understanding the performance of various architectures. Vision transformers provide at par results for the fingerprint PAD compared to CNNs with more extensive training duration suggesting its promising nature. In addition, the chapter presents the results for a partial open-set protocol and a true open-set protocol analysis where neither the capture sensor nor the material in the testing set is known at the training phase. With the true open-set protocol analysis, this chapter presents the weakness of both CNN architectures and vision transformers in scaling up to unknown test data, i.e., generalizability challenges.