Detecting Altered Fingerprints

Abstract

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.