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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%).

In this work, an improved ultrasound technique for biometric recognition is proposed. It allows to acquire simultaneously two biometric characteristics: 3D Hand Geometry and 3D Palmprint. An open research platform is employed as ultrasound imaging system. A commercial high frequency probe is moved in the elevation direction over the region of interest by an automated scanning system based on numeric controlled pantograph. Experimental results are presented and the advantages of the proposed technique are highlighted and discussed.

In this work, some possible procedures to extract both 2D and 3D palmprints from the same experimental 3D ultrasound image of the human palm are presented. The ultrasound system used to achieve the 3D images is composed of a CNC commercial pantograph, which moves a high frequency (12 MHz) ultrasound probe along its elevation direction to cover the desired area of the human palm. The ULtrasound Advanced Open Platform (ULA-OP) is employed as ultrasound imaging system.

An ultrasound technique for extracting 3D palmprints has been recently proposed. A drawback of that method relies in the small volume acquired, which is limited by the aperture of the array.

In this paper we analyze the vulnerability to hill-climbing attacks of a biometric recognition system based on electroencephalography (EEG). It is assumed that an attacker can access the scores produced by the employed matcher, and use them to control the generation of synthetic EEG templates until achieving a successful authentication. To this aim, different general approaches relying on function optimization are evaluated and compared in terms of authentication success rate and average number of required attempts. The possibility of increasing the system robustness against this kind of attacks, without significantly affecting its recognition performance, is also investigated.

In the present work, and enhanced ultrasound technique for extracting 3D palmprint for biometric recognition purposes is presented. The ULtrasound Advanced Open Platform (ULA-OP) is employed as ultrasound imaging system. A commercial high frequency (12 MHz) linear array is moved in the elevation direction by an automated scanning system based on numeric controlled pantograph, which guarantees stable and repeatable measurements. The 3D data are then acquired and elaborated in MATLAB environment to provide several renderings of the 3D palmprint. The experimental results are compared with those acquired with conventional techniques.

Although biometric recognition systems provide many advantages over traditional recognition methods, they can be vulnerable to specific attacks which may considerably decrease their security. In this paper we focus on the hill-climbing attack which is peculiar of biometric systems. Specifically, we evaluate the effectiveness of general approaches relying on parametric functions optimization for performing this kind of attack, and propose possible countermeasures which can be used for increasing the system robustness without significantly affecting its recognition performance. An application to on-line signature biometrics is taken into account to test both the proposed attacks and some possible countermeasures.