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

Abstract

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.

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“…The first vulnerable point of the mechanism, exploited by attackers who want to penetrate the system through various techniques and attack tools to manipulate digital facial features, present from the early phase of the authentication process is found at the biometric sensor level. There are well-known attacks such as the presentation attack [61], also known as the spoofing attack which involves various misleading means such as photographic paper, high-resolution photos, video footage, or authentication masks (2D, 3D), the morphing attack [62] which consists of creating artificial facial biometric samples by integrating several faces into a single face, and Deepfake technologies based on artificial intelligence algorithms and deep learning which aim to create fake digital images as persuasive as possible or videos that achieve similarity between two users.…”

“…The first vulnerable point of the mechanism, exploited by attackers who want to penetrate the system through various techniques and attack tools to manipulate digital facial features, present from the early phase of the authentication process is found at the biometric sensor level. There are well-known attacks such as the presentation attack [61], also known as the spoofing attack which involves various misleading means such as photographic paper, high-resolution photos, video footage, or authentication masks (2D, 3D), Another vulnerable point of the mechanism is the biometric database, which can be hacked, either by coercion of the system administrator, by completely bypassing the security rings around the database, or by an external attacker who can steal the administrator privileges and the access credentials to directly modify the data stored in the database. Various information can also be injected into the database that can compromise it or alter the biometric templates so that the system can no longer function at optimal parameters.…”

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