Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

Inventions

Shraddha Phansalkar¹  Sudeep D. Thepade²  Ketan Kotecha³  Shilpa Gite⁴  Smita Khade⁴  Swati Ahirrao⁴ 

[1] Department of Computer Engineering, MIT Art, Design and Technology University, Pune 412201, India;Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune 411044, India;Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 412115, India;Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India;

关键词: biometric authentication;    iris;    liveness detection;    identification;    machine learning;    deep learning;   

DOI  :  10.3390/inventions6040065

来源: DOAJ

【 摘 要 】

Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.

【 授权许可】

Unknown