Likelihood-Ratio-Based Biometric Verification

The paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that, for single-user verification, the likelihood ratio is optimal. Second, we show that, under some general conditions, decisions based on posterior probabilities and likelihood ratios are equivalent and result in the same receiver operating curve. However, in a multi-user situation, these two methods lead to different average error rates. As a third result, we prove theoretically that, for multi-user verification, the use of the likelihood ratio is optimal in terms of average error rates. The superiority of this method is illustrated by experiments in fingerprint verification. It is shown that error rates below 10/sup -3/ can be achieved when using multiple fingerprints for template construction

Pitfall of the Detection Rate Optimized Bit Allocation within template protection and a remedy

One of the requirements of a biometric template protection system is that the protected template ideally should not leak any information about the biometric sample or its derivatives. In the literature, several proposed template protection techniques are based on binary vectors. Hence, they require the extraction of a binary representation from the real- valued biometric sample. In this work we focus on the Detection Rate Optimized Bit Allocation (DROBA) quantization scheme that extracts multiple bits per feature component while maximizing the overall detection rate. The allocation strategy has to be stored as auxiliary data for reuse in the verification phase and is considered as public. This implies that the auxiliary data should not leak any information about the extracted binary representation. Experiments in our work show that the original DROBA algorithm, as known in the literature, creates auxiliary data that leaks a significant amount of information. We show how an adversary is able to exploit this information and significantly increase its success rate on obtaining a false accept. Fortunately, the information leakage can be mitigated by restricting the allocation freedom of the DROBA algorithm. We propose a method based on population statistics and empirically illustrate its effectiveness. All the experiments are based on the MCYT fingerprint database using two different texture based feature extraction algorithms

Model-based design for selecting fingerprint recognition algorithms for embedded systems

Most of contributions for biometric recognition solutions (and specifically for fingerprint recognition) are implemented in software on PC or similar platforms. However, the wide spread of embedded systems means that fingerprint embedded systems will be progressively demanded and, hence, hardware dedicated solutions are needed to satisfy their constraints. CAD tools from Matlab-Simulink ease hardware design for embedded systems because automatize the design process from high-level descriptions to device implementation. Verification of results is set at different abstraction levels (high- level description, hardware code simulation, and device implementation). This paper shows how a design flow based on models facilitates the selection of algorithms for fingerprint embedded systems. In particular, the search of a solution for directional image extraction suitable for its application to singular point extraction is detailed. Implementation results in terms of area occupation and timing are presented for different Xilinx FPGAs.Ministerio de Economía y Competitividad TEC2011-24319Junta de Andalucía P08-TIC-03674Comunidad Europea FP7-INFSO-ICT-24885

Detection of Singular Points from Fingerprint Images Using an Innovative Algorithm

Fingerprint scrutiny is typically based on the location and pattern of detected singular points in the images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. In this report, there is an innovative algorithm for singular points detection. After an initial detection using the conventional Poincare Index method, a so-called DORIVAC feature is used to remove spurious singular points. Then, the optimal combination of singular points is selected to minimize the difference between the original orientation field and the model-based orientation field reconstructed using the singular points. A core-delta relation is used as a global constraint for the final selection of singular points. Keywords: Orientation field, Poincare´ Index, Singular points, topological structur