Information Security Journal A Global Perspective · 2021 · 32 citations · 57 references
EngineeringMachine LearningBiometric PrivacyInformation SecurityBiometricsInformation ForensicsTemplate ProtectionHardware SecurityData SciencePattern RecognitionSoft BiometricsDeep Learning ApproachesIdentity-based SecurityComputer ScienceBiometric SystemsDeep LearningData SecurityBiometric SecurityCryptographyHuman IdentificationSecurity
Biometric systems identify individuals based on unique traits such as the face, fingerprints, iris etc. The main objective of the study is to understand the role of deep learning in the process of authentication as well as its application in the enhancement of security of biometric systems. We highlight the studies using deep learning approaches to authenticate enrolled users under ideal and non-ideal environmental conditions. We summarize these approaches and explore the challenges that continue to restrict the full potential of biometric systems. The foremost are: building robust algorithms for authentication, ensuring the security of enrolled templates and protecting systems against spoofing attacks. In this paper, we review the performance achieved by various studies in overcoming the aforesaid challenges, along with the potential improvements and future directions in this domain.
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DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato et al. · 2014 · 6.5K citations
Convolutional Neural Network, Engineering, Machine Learning +18
Omkar Parkhi, Andrea Vedaldi, Andrew Zisserman · 2015 · 5K citations
Face Detection, Convolutional Neural Network, Facial Recognition System +14