Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS. This paper proposes a method for detection and localization of fingerprint alterations. Our main contributions are: (i) design and train CNN models on fingerprint images and minutiae-centered local patches in the image to detect and localize regions of fingerprint alterations, and (ii) train a Generative Adversarial Network (GAN) to synthesize altered fingerprints whose characteristics are similar to true altered fingerprints. A successfully trained GAN can alleviate the limited availability of altered fingerprint images for research. A database of 4,815 altered fingerprints from 270 subjects, and an equal number of rolled fingerprint images are used to train and test our models. The proposed approach achieves a True Detection Rate (TDR) of 99.24 at a False Detection Rate (FDR) of 2 , outperforming published results. The altered fingerprint detection and localization model and code, and the synthetically generated altered fingerprint dataset will be open-sourced.
The widespread deployment of Automated Fingerprint Identification Systems (AFIS) in law enforcement and border control applications has prompted some individuals with criminal background to evade identification by purposely altering their fingerprints. Available fingerprint quality assessment software cannot detect most of the altered fingerprints since the implicit image quality does not always degrade due to alteration. In this paper, we classify the alterations observed in an operational database into three categories and propose an algorithm to detect altered fingerprints. Experiments were conducted on both real-world altered fingerprints and synthetically generated altered fingerprints. At a false alarm rate of 7 , the proposed algorithm detected 92 of the altered fingerprints, while a well-known fingerprint quality software, NFIQ, only detected 20 of the altered fingerprints.