Abstract
The Presentation attacks on biometric systems have become a major concern in the cyber world. The literature reveals that these systems are vulnerable to spoofing or presentation attacks (PAs) which sometimes result in complete failure of the recognition system. To combat against PAs, the anti-spoofing or Presentation attack detection (PAD) methods have been developed to check the liveness of the fingerprint modality. However, with the emergence of artificial intelligence, several software-based countermeasures have been proposed by the research community. Depending on the type feature extraction mechanism the PAD techniques are categorized into two main types namely; Machine learning (ML) and Deep Learning (DL) based approaches. This paper aims to present a review of existing work on the fingerprint PAD mechanism which has been carried out. The paper discusses the various fingerprint artifacts, state-of-the-art ML and DL based PAD algorithms, and publically available benchmark datasets. Furthermore, we discuss the major research challenges and future work that need to be addressed in this active field of fingerprint liveness detection.
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