Biblio

Biometrics as a means of personal authentication has demonstrated strong viability in the past decade. However, directly deriving a unique cryptographic key from biometric data is a non-trivial task due to the fact that biometric data is usually noisy and presents large intra-class variations. Moreover, biometric data is permanently associated with the user, which leads to security and privacy issues. Cancellable biometrics and bio-cryptosystem are two main branches to address those issues, yet both approaches fall short in terms of accuracy performance, security, and privacy. In this paper, we propose a Bio-Crypto system on fingerprint Cancellable template (Bio-CanCrypto), which bridges cancellable biometrics and bio-cryptosystem to achieve a middle-ground for alleviating the limitations of both. Specifically, a cancellable transformation is applied on a fixed-length fingerprint feature vector to generate cancellable templates. Next, an LDPC coding mechanism is introduced into a reusable fuzzy extractor scheme and used to extract the stable cryptographic key from the generated cancellable templates. The proposed system can achieve both cancellability and reusability in one scheme. Experiments are conducted on a public fingerprint dataset, i.e., FVC2002. The results demonstrate that the proposed LDPC coded reusable fuzzy extractor is effective and promising.

With the emergence of biometric technology in various applications, such as access control (e.g. mobile lock/unlock), financial transaction (e.g. Alibaba smile-to-pay) and time attendance, the development of biometric system attracts increasingly interest to the developers. Despite a sound biometric system gains the security assurance and great usability, it is a rather challenging task to develop an effective biometric system. For instance, many public available biometric APIs do not provide sufficient instructions / precise documentations on the usage of biometric APIs. Many developers are struggling in implementing these APIs in various tasks. Moreover, quick update on biometric-based algorithms (e.g. feature extraction and matching) may propagate to APIs, which leads to potential confusion to the system developers. Hence, we conduct an empirical study to the problems that the developers currently encountered while implementing the biometric APIs as well as the issues that need to be addressed when developing biometric systems using these APIs. We manually analyzed a total of 500 biometric API-related posts from various online media such as Stack Overflow and Neurotechnology. We reveal that 1) most of the problems encountered are related to the lack of precise documentation on the biometric APIs; 2) the incompatibility of biometric APIs cross multiple implementation environments.