Secure Biometrics: Concepts, Authentication Architectures and Challenges

Abstract

BIOMETRICS are an important and widely used class of methods for identity verification and access control. Biometrics are attractive because they are inherent properties of an individual. They need not be remembered like passwords, and are not easily lost or forged like identifying documents. At the same time, bio- metrics are fundamentally noisy and irreplaceable. There are always slight variations among the measurements of a given biometric, and, unlike passwords or identification numbers, biometrics are derived from physical characteristics that cannot easily be changed. The proliferation of biometric usage raises critical privacy and security concerns that, due to the noisy nature of biometrics, cannot be addressed using standard cryptographic methods. In this article we present an overview of "secure biometrics", also referred to as "biometric template protection", an emerging class of methods that address these concerns.

16 pages, 11 figures, 1 table

Cited by in corpus (4)

• Deep Hashing for Secure Multimodal Biometrics
• Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification
• Controllable Identifier Measurements for Private Authentication with Secret Keys
• Cancelable Biometric Template Generation Using Random Feature Vector Transformations