Fingerprint Liveness Detection Schemes: A Review on Presentation Attack

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.

KEYWORDS:

• Fingerprint spoofing
• presentation attack
• fingerprint anti-spoofing techniques
• hardware-based approach
• software-based approach
• spoof generalisation

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Notes on contributors

D. S. Ametefe

Divine Senanu Ametefe received his B.Tech. Degree in Telecommunication Engineering from Ghana Technology University College (GTUC), Ghana, in the year 2017. He was honored with an MSc. Degree in Telecommunication and Information Engineering by Universiti Teknologi MARA (UiTM) in Malaysia, in 2019. He is currently pursuing a PhD program in the field of Electrical Engineering at Universiti Teknologi MARA (UiTM), Malaysia. His areas of interest include fingerprint biometrics, pattern recognition, neural networks, deep learning, and the Internet of things.

S. S. Sarnin

Suzi Seroja Sarnin received her Bachelor of Electrical and Electronics (B.Eng.) in the field of Communication from the Universiti Teknologi Malaysia, Skudai, Malaysia, in 1999. She completed her Master in Microelectronics (MSc) from the Universiti Kebangsaan Malaysia in 2005. She has a PhD in Electrical Engineering from Universiti Teknologi MARA, Shah Alam, Malaysia, and is currently a senior lecturer at the Universiti Teknologi MARA and collaborates actively with researchers in several disciplines of Electrical Engineering.

D. M. Ali

Darmawaty Mohd Ali is an Associate Professor at Universiti Teknologi MARA (UiTM), Selangor, Malaysia. She obtained her Ph.D. in 2012 from Universiti Malaya, Malaysia. She received her Master of Electrical Engineering in 2002 from Universiti Teknologi Malaysia. Previously, she earned her first degree from Universiti Kebangsaan Malaysia with Honours, in Electrical, Electronic, and System, graduating in 1999. She is the head of Wireless Communication Technology (WiCOT) Research Interest Group (RIG), and her research interests include Wireless Access Technology and Quality of Service in Wireless Broadband.

M. Z. Zaheer

Muhammad Zaigham Zaheer is currently a PhD candidate at the University of Science and Technology, Daejeon, Korea. He is also associated with Electronics and Telecommunications Research Institute, Daejeon, Korea, as a student researcher and with ETH Zurich as a visiting researcher. Previously, he received his MS degree from Chonnam National University, Gwangju, Korea, in 2017 and his BS degree from Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan, in 2012. His current research interests include computer vision, anomaly detection in images/videos, semi-supervised/self-supervised learning, and video object segmentation.