Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions

• Published in: Big data and cognitive computing 2023-03, Vol.7 (1), p.37
• Main Authors: Khairnar, Smita, Gite, Shilpa, Kotecha, Ketan, Thepade, Sudeep D.
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial intelligence; artificial intelligence (AI); Automation; Bibliometrics; Biometric identification; Biometrics; Biometry; Business metrics; Competition; Data encryption chips; Data mining; Datasets; Deep learning; domain adaptation; Evaluation; explainable AI (XAI); Explainable artificial intelligence; face liveness detection (FLD); Face recognition; Facial recognition technology; Feature extraction; Forecasts and trends; Identity; Literature reviews; Liu, Timothy; Machine learning; Neural networks; Researchers; Spoofing; Systematic review
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc7010037

Biometrics has been evolving as an exciting yet challenging area in the last decade. Though face recognition is one of the most promising biometrics techniques, it is vulnerable to spoofing threats. Many researchers focus on face liveness detection to protect biometric authentication systems from spoofing attacks with printed photos, video replays, etc. As a result, it is critical to investigate the current research concerning face liveness detection, to address whether recent advancements can give solutions to mitigate the rising challenges. This research performed a systematic review using the PRISMA approach by exploring the most relevant electronic databases. The article selection process follows preset inclusion and exclusion criteria. The conceptual analysis examines the data retrieved from the selected papers. To the author, this is one of the foremost systematic literature reviews dedicated to face-liveness detection that evaluates existing academic material published in the last decade. The research discusses face spoofing attacks, various feature extraction strategies, and Artificial Intelligence approaches in face liveness detection. Artificial intelligence-based methods, including Machine Learning and Deep Learning algorithms used for face liveness detection, have been discussed in the research. New research areas such as Explainable Artificial Intelligence, Federated Learning, Transfer learning, and Meta-Learning in face liveness detection, are also considered. A list of datasets, evaluation metrics, challenges, and future directions are discussed. Despite the recent and substantial achievements in this field, the challenges make the research in face liveness detection fascinating.