| Summary: | The robustness of automatic fingerprint identification systems (AFIS) against presentation attacks, a pressing concern in biometric security, is critically dependent on the efficacy of fingerprint liveness detection (FLD) methodologies. This article presents a systematic review of the latest advancements in FLD, focusing on publications from 2022 to 2023. A meticulous analysis of 27 studies sourced from the Web of Science (WoS) and ScienceDirect databases reveals significant strides in FLD techniques, aimed at enhancing the resilience of AFIS against increasingly sophisticated spoofs made from everyday materials like wood glue, playdoh, and latex. These innovative approaches, encompassing advanced machine learning algorithms, IoT-based multimodal detection, and novel material-based detection methods, reflect a concerted effort to counteract the evolving tactics of fraudulent entities. Despite these technological advancements, the study identifies ongoing challenges that impede the full-proof security of AFIS. These include data privacy concerns in hardware-based systems, the emergence of thin-layered and subtle spoofing methods, the complexities of puppet attacks, and difficulties in cross-material detection, all of which hinder the generalization of live fingerprint identification. The study also delves into the sector-specific implications of these developments in critical domains such as law enforcement, banking, and personal security, underscoring the balance between enhanced security and privacy concerns, especially in systems employing multiple biometric modalities. This study not only highlights the current achievements in FLD but also underscores the necessity for continued research and development. The objective is to address these persisting challenges and to ensure the robustness of biometric security systems in the face of rapidly evolving threats, thereby bolstering their integrity and reliability. © The Institution of Engineers (India) 2024.
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