In massive biometric identification systems, response times mainly depends on the database searching algorithms. Thus, in large databases, an increment in the simultaneous queries traffic becomes a critical factor. This paper proposes an algorithm based on the use of a graphic processing unit to solve the exhaustive similarity search for the mass identification of finger veins, using the binary pattern descriptor of the local vertical line and the Hamming distance. The proposed approach reduces the computation time of the searching process over high query traffic by solving each query with a different processing block. The proposed method allows the identification of individuals in a database of 1 million elements, which is the largest database used for finger-vein identification. Experimental results show that our proposed method resolves up to 28 queries simultaneously (over a database of one million individuals) within a time lower than 3 seconds and achieving a speed-up of 283x. To our knowledge, our work is the first implementation of finger-vein recognition on a general-purpose graphics processing unit, which is the main contribution of this document.
The challenge of improving the performance of current processors is achieved by increasing the integration scale. This carries a growing vulnerability to transient faults, which increase their impact on multicore clusters running large scientific parallel applications. The requirement for enhancing the reliability of these systems, coupled with the high cost of rerunning the application from the beginning, create the motivation for having specific software strategies for the target systems. This paper introduces SMCV, which is a fully distributed technique that provides fault detection for message-passing parallel applications, by validating the contents of the messages to be sent, preventing the transmission of errors to other processes and leveraging the intrinsic hardware redundancy of the multicore. SMCV achieves a wide robustness against transient faults with a reduced overhead, and accomplishes a trade-off between moderate detection latency and low additional workload.
Nowadays, individual identification is a problem in many private companies, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. Finger vein recognition is a modern biometric technique, which has several advantages, especially in terms of security and accuracy. However, image deformations and time efficiency are two of the major limitations of state-of-the-art contributions. In spite of affine transformations produced during the acquisition process, the geometric structure of finger vein images remains invariant. This consideration of the symmetry phenomena presented in finger vein images is exploited in the present work. We combine an image enhancement procedure, the DAISY descriptor, and an optimized Coarse-to-fine PatchMatch (CPM) algorithm under a multicore parallel platform, to develop a fast finger vein recognition method for real-time individuals identification. Our proposal provides an effective and efficient technique to obtain the displacement between finger vein images and considering it as discriminatory information. Experimental results on two well-known databases, PolyU and SDUMLA, show that our proposed approach achieves results comparable to deformation-based techniques of the state-of-the-art, finding statistical differences respect to non-deformation-based approaches. Moreover, our method highly outperforms the baseline method in time efficiency.
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