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In this paper, we present a novel biometric cryptosystem obtaining perfect security, that is not leaking any information about the employed secret key from the knowledge of the stored helper data. While similar purposes have already been sought in the literature, the approaches proposed so far have been evaluated in terms of recognition performance under the unrealistic assumption of ideal statistical distributions for the considered biometric data. Conversely, in this paper, we investigate the applicability of the proposed framework to practical scenarios while managing a trade-off between privacy and recognition performance. This goal has been achieved by introducing a class of transformation functions enforcing zero-leakage secrecy, by designing an adaptive strategy for embedding the secret key bits into the selected features, and by developing a system parameters optimization strategy with respect to security, recognition performance, and privacy. Experimental tests conducted on real fingerprint data prove the effectiveness of the proposed scheme.

In this paper, we introduce a novel minutiae-based matching algorithm for fingerprint recognition. The method is built on an elegant and straightforward mathematical formulation: the minutiae set is represented by a train of complex pulses and the matching algorithm is based on a simple cross-correlation. We propose two different implementations. The first one exploits the intrinsic sparsity of the signal representing the minutiae set in order to construct an efficient implementation. The other relies on the Fourier transform to build a fixed-length representation, being thus suitable to be used in many biometric crypto-systems. The proposed method exhibits performance comparable with NIST's Bozorth3, that is a standard de facto for minutiae matching, but it shows to be more robust with cropped fingerprints.

Most commercially available ultrasound imaging systems currently implement the delay and sum (DAS) beamforming. Alternative beamformers have been presented, offering higher performance at the expense of computational complexity, which has so far limited their practical real-time implementation. In particular, the Filtered-Delay Multiply and Sum (F-DMAS) beamformer, which adds the computation of signed square roots, absolute values and multiplications to DAS, was recently shown to be effective in improving resolution, dynamic range and overall image quality. In this work, we present the real-time implementation of the F-DMAS beamformer on the ULA-OP 256 research system. This is obtained through a reformulation of the F-DMAS algorithm that allows reducing its computational cost. Compared to DAS, F-DMAS images of a commercial phantom captured in real-time showed higher CR (up to +7 dB), better spatial resolution (up to 30%), but lower CNR (-40%).

Biometric recognition systems, despite the advantages provided with respect to traditional authentication methods, have some peculiar weaknesses which may allow an attacker being falsely recognized or accessing users’ personal data. Among such vulnerabilities, in this paper, we speculate on the hill-climbing attack, i.e., the possibility for an attacker to exploit the scores produced by the matcher with the goal of generating synthetic biometric data, which could allow a false acceptance. More in detail, we focus on multibiometrics systems and investigate about the robustness of different system architectures, both parallel and serial fusion schemes, against the hill-climbing attack. Nonuniform quantization is also evaluated as a possible countermeasure for limiting the effectiveness of the considered attacks in terms of recognition success rate and average number of required attempts without affecting the recognition performance.