Fingerprint Liveness Detection Schemes: A Review on Presentation Attack

Abstract

ABSTRACT Extant literature has highlighted the vulnerability of Automatic Fingerprint Identification System (AFIS) to various forms of attacks, indicating presentation attack as the most predominant. This form of attack involves the malicious utilization of ubiquitous materials such as silicone, gelatin, playdoh, among many others, to fabricate synthetic fingerprints to circumvent AFIS. As a result, various studies have posited some countermeasures, encompassing the use of hardware- based and software-based techniques. The hardware-based methods necessitate the integration of supplementary sensors for capturing other live human traits such as pulse rate, odour etc. Conversely, the software-based methods are focused on feature extraction and deep learning schemes. However, despite the robustness of the software-based approach as compared to the hardware-based technique, both schemes are still faced with an immense challenge in developing fingerprint spoof generalized models to mitigate the concern of cross-material (novel) detection. As a result, various studies have highlighted that the issue of fingerprint spoofing should be treated an” opened-set problem” (training only live fingerprints), rather than a” closed-set problem” (training live & spoof), birthing the development of fingerprint one-class classifiers. This article presents a comprehensive detail on fingerprint spoofing, extant countermeasures and the challenge still faced by fingerprint spoof detectors.