Biometric Anti-spoofing Technique Using Randomized 3D Multi-Modal Traits

Despite their advantages over password-based and token-based authentication, Biometric Authentication Systems (BAS) are not perfect. They are particularly vulnerable to spoofing, also called Suspicious Presentation (SP) attacks whereby an impostor presents a fake trait to the biometric scanner during verification. Spoofing has a critical impact on system security leading to a trust deficit on biometric systems with weak anti-spoofing mechanisms. Mitigating biometric spoofing is a possibility, hence several techniques have evolved in recent times including multi-biometrics, biometric cryptography and Liveness Detection (LD) also called Suspicious Presentation Detection (SPD). Unfortunately, nearly all known LD techniques exhibit a fundamental set of flaws – they are mostly uni-modal, easily predictable by a well-equipped impostor, and can be circumvented by well-crafted SP attacks. This paper presents the Multi-Modal Random Trait Biometric Liveness Detection System (MMRTBLDS) framework, as an alternative approach that implements LD using multiple traits each acquired from separate modalities of the same subject combined in a randomized manner. The strength of the framework lays in the impostor’s inability to accurately predict the exact set of randomized trait parameter combinations in advance of LD. The framework employs a 3D simulation of fifteen liveness parameters, composed of three each from finger, face and iris traits, based on random number generation. Simulation results obtained using 125 distinct randomized combinations show significant improvements in biometric authentication security with a system efficiency of 99.2%.