Research on biometrics has noticeably increased. However, no single bodily or behavioral feature is able to satisfy acceptability, speed, and reliability constraints of authentication in real applications. The present trend is therefore toward multimodal system. In this paper, we deal with some core issues related to the design of these systems and propose a novel modular framework, namely, novel approaches for biometric systems (NABS) that we have implemented to address them. NABS proposal encompasses two possible architectures based on the comparative speeds of the involved biometries.
It also provides a novel solution for the data normalization problem, with the new quasi-linear sigmoid (QLS) normalization function. This function can overcome a number of common limitations, according to the presented experimental comparisons.
A further contribution is the system response reliability (SRR) index to measure response confidence. Its theoretical definition allows taking into account the gallery composition at hand in assigning a system reliability measure on a single-response basis.
The unified experimental setting aims at evaluating such aspects both separately and together, using face, ear, and fingerprint as test biometries. The results provide a positive feedback for the overall theoretical framework developed herein. Since NABS is designed to allow both a flexible choice of the adopted architecture, and a variable compositions and/or substitution of its optional modules, i.e., QLS and SRR, it can support different operational settings.
The previous work in the area of encryption-based security of biometric templates tends to model the problem as that of building a classification system that separates the genuine and impostor samples in the encrypted domain. However, a strong encryption mechanism destroys any pattern in the data, which adversely affects the accuracy of verification. Hence, any such matching mechanism necessarily makes a compromise between template security (strong encryption) and accuracy (retaining patterns in the data). The primary difference in our approach is that we are able to design the classifier in the plain feature space, which allows us to maintain the performance of the biometric itself, while carrying out the authentication on data with strong encryption, which provides high security/privacy.
Over the years a number of attempts have been made to address the problem of template protection and privacy concerns and despite all efforts, puts it, “a template protection scheme with provable security and acceptable recognition performance has thus far remained elusive”. In this section, we will look at the existing work in light of this security-accuracy dilemma, and understand how this can be overcome by communication between the authenticating server and the client. Detailed reviews of the work on template protection can be found.
Blind authentication is able to achieve both strong encryption-based security as well as accuracy of a powerful classifiers such as support vector machines (SVMs) and neural networks. While the proposed approach has similarities to the blind vision scheme for image retrieval, it is far more efficient for the verification task. Blind Authentication addresses all the concerns mentioned.
The ability to use strong encryption addresses template protection issues as well as privacy concerns.
Non-repudiable authentication can be carried out even between non-trusting client and server using a trusted third party solution.
It provides provable protection against replay and client side attacks even if the keys of the user are compromised.
As the enrolled templates are encrypted using a key, one can replace any compromised template, providing revocability, while allaying concerns of being tracked.
The framework is generic in the sense that it can classify any feature vector, making it applicable to multiple biometrics. Moreover, as the authentication process requires someone to send an encrypted version of the biometric, the nonrepudiable nature of the authentication is fully preserved, assuming that spoof attacks are prevented. The proposed approach does not fall into any of the categories. This work opens a new direction of research to look at privacy preserving biometric authentication.
1. Authentication module
2. Blind encryption
3. Encrypted data forwarding
1. Blind decryption
2. Biometric verification
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