Secure biometric image sensor and authentication scheme based on compressed sensing

Abstract

It is important to ensure the security of biometric authentication information, because its leakage causes serious risks, such as replay attacks using the stolen biometric data, and also because it is almost impossible to replace raw biometric information. In this paper, we propose a secure biometric authentication scheme that protects such information by employing an optical data ciphering technique based on compressed sensing. The proposed scheme is based on two-factor authentication, the biometric information being supplemented by secret information that is used as a random seed for a cipher key. In this scheme, a biometric image is optically encrypted at the time of image capture, and a pair of restored biometric images for enrollment and verification are verified in the authentication server. If any of the biometric information is exposed to risk, it can be reenrolled by changing the secret information. Through numerical experiments, we confirm that finger vein images can be restored from the compressed sensing measurement data. We also present results that verify the accuracy of the scheme.

Microstructure Informed Tractography: Pitfalls and Open Challenges

“…Actually, the same issue may arise also without such threshold if a form of regularization is naively applied. As an example, using Φ( x ) = || x || 1 for promoting sparsity (Candès et al, 2006 ; Donoho, 2006 ) in the tractogram, small coefficients may tend to be suppressed depending on how the specific algorithm for solving Equation (2) handles highly correlated atoms. Thus, false negatives may be generated also in this case and different regularization functions may lead to even more unpredictable results; this is why we adopted classical least squares in all our experiments.…”

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

“…This can be achieved by minimizing the KL-divergence between the distribution of the external states estimated from the all-to-all network, and the distribution estimated from a given connection structure (i.e., , see Section Optimality of Connectivity for details). However, this calculation requires combinatorial optimization, and local approximation is generally difficult (Donoho, 2006 ), thus expectedly the brain employs some heuristic alternatives. Experimental results indicate that synaptic connections and weights are often representing similar features.…”

Compressed sensing approach for wrist vein biometrics

“…This approach was demonstrated in refs. . In this case we do not have to obtain and store in device’s memory actual vein pattern images thus protecting the user against stealing of the personal data.…”

“…The reconstruction time in our experiments is more or less equal for the both types of SLM with value ~10 s for 500 masks used in 60 × 60 pixels image reconstruction. This value can be significantly decreased by using fast algorithms and optimization procedures used in software transfer to real device prototype; however, according to the above‐mentioned works , one can skip reconstruction procedure and for the authentication purposes use only a 1D raw detector signal thus increasing the whole system’s performance.…”

“…Moreover, storing vein pattern images in the device memory is a risk to the personal data. A compressed sensing (CS) approach can help us to solve these issues; its advantages have been demonstrated in works , where the authors describe a numerical simulation of authentication procedure based upon finger‐vein image reconstruction, and discuss issues of accuracy and potential improvements of security. As for a practical implementation of the CS‐based approach for a wearable wrist‐type device, one can consider an original scheme , which is, however, not compact enough mainly due to a digital mirror device (DMD) used as the spatial light modulation (SLM) unit.…”