Efficient fingerprint matching on smart cards for high security and privacy in smart systems

Abstract

Smart control access to any service is at the very basis of any smart project. Biometrics have been used as a solution for service access control an any smart system, for many years now. However, the simple use of biometrics cannot be considered as final and perfect solution. Most problems are related to the data transmission method between the medias, for which the users require access and the servers, wherein the biometric data, captured upon registration, are stored. In this paper, we use smart cards as an effective yet efficient solution to this critical data storage problem. Furthermore, fingerprints have been used as a human identifier for some time now. This biometric is considered one of the most reliable to distinguish a person from another as its unique yet perfectly stable over time. In this work, we propose an efficient implementation of fingerprint verification on smart cards. For this implementation, the matching is done on-card. Thus, the biometric characteristics are always kept in the owner’s card, guaranteeing the maximum security and privacy. The proposed implementation is based on the Skin Elasticity Tolerant Algorithm (SETA), which uses minutiae to implement fingerprints matching. It consists of finding the best alignment between the compared fingerprints before proceeding with the comparison step to maximize the score that quantifies the quality of the matching. Although efficient, SETA is not suitable for card implementation because it requires a lot of dynamic storage memory to store the counters of all possible rotations and translations. In order to make the implementation on smart cards viable mitigating the problem of the volume of storage required to store the translations and rotations, we subdivide the search space into small subspaces. Thus, only the translations and rotations that fall within to the handled subspace at each instant are considered. The implementation on smart cards is evaluated for known fingerprint datasets. Several settings are tested, establishing that the implementation is suitable for real-time applications.