Dedicated Hardware IP Module for Fingerprint Recognition

This work presents a dedicated hardware IP module for fingerprints recognition based on a feature, named QFingerMap, which is very suitable for VLSI design. FPGA implementation results of the IP module are given. A demonstrator has been developed to evaluate the IP module behavior in a real scenario.Gobierno de España y fondos europeos FEDER - TEC2014-57971-R, TEC2011-24319, IPT-2012-0695 y RTC-2014-2932-8V Plan Propio de Investigación de la Universidad de Sevill

Demonstrator of a fingerprint recognition algorithm into a low-power microcontroller

A demonstrator has been developed to illustrate the performance of a lightweight fingerprint recognition algorithm based on the feature QFingerMap16, which is extracted from a window of the directional image centered at the convex core of the fingerprint. The algorithm has been implemented into a lowpower ARM Cortex-M3 microcontroller included in a Texas Instruments LaunchPad CC2650 evaluation kit. It has been also implemented in a Raspberry Pi 2 so as to show the results obtained at the successive steps of the recognition process with the aid of a Graphical User Interface (GUI). The algorithm offers a good tradeoff between power consumption and recognition accuracy, being suitable for authentication on wearables

A fingerprint biometric cryptosystem in FPGA

Comunicación presentada al ICIT 2015 celebrado en Sevilla del 17 al 19 de marzo de 2015This paper presents the implementation of a complete fingerprint biometric cryptosystem in a Field Programmable Gate Array (FPGA). This is possible thanks to the use of a novel fingerprint feature, named QFingerMap, which is binary, length-fixed, and ordered. Security of Authentication on FPGA is further improved because information stored is protected due to the design of a cryptosystem based on Fuzzy Commitment. Several samples of fingers as well as passwords can be fused at feature level with codewords of an error correcting code to generate non-sensitive data. System performance is illustrated with experimental results corresponding to 560 fingerprints acquired in live by an optical sensor and processed by the system in a Xilinx Virtex 6 FPGA. Depending on the realization, more or less accuracy is obtained, being possible a perfect authentication (zero Equal Error Rate), with the advantages of real-time operation, low power consumption, and a very small devicePeer reviewe

Efficient parallelization on GPU of an image smoothing method based on a variational model

Medical imaging is fundamental for improvements in diagnostic accuracy. However, noise frequently corrupts the images acquired, and this can lead to erroneous diagnoses. Fortunately, image preprocessing algorithms can enhance corrupted images, particularly in noise smoothing and removal. In the medical field, time is always a very critical factor, and so there is a need for implementations which are fast and, if possible, in real time. This study presents and discusses an implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on graphics processing units techniques. The use of these techniques facilitates the quick and efficient smoothing of images corrupted by noise, even when performed on large-dimensional data sets. This is particularly relevant since GPU cards are becoming more affordable, powerful and common in medical environments

A PUF-and biometric-based lightweight hardware solution to increase security at sensor nodes

Security is essential in sensor nodes which acquire and transmit sensitive data. However, the constraints of processing, memory and power consumption are very high in these nodes. Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required. In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node. In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes. Since biometric data of individuals are sensitive, they are also obfuscated with PUFs. Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol. One factor is the unique physical identity of the trusted sensor node that measures them. The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission. Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node. Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node. Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases.Ministerio de Economía, Industria y Competitividad TEC2014-57971-R, TEC2017-83557-

A hardware solution for real-time intelligent fingerprint acquisition

Pubblished online.-- Special Issue.-- El pdf del artículo es la versión post-print.The first step in any fingerprint recognition system is the fingerprint acquisition. A well-acquired fingerprint image results in high-resolution accuracy and low computational effort of processing. Hence, it is very useful for the recognition system to evaluate recognition confidence level to request new fingerprint samples if the confidence level is low, and to facilitate recognition process if the confidence level is high. This paper presents a hardware solution to ensure a successful and friendly acquisition of the fingerprint image, which can be incorporated at low cost into an embedded fingerprint recognition system due to its small size and high speed. The solution implements a novel technique based on directional image processing that allows not only the estimation of fingerprint image quality, but also the extraction of useful information (in particular, singular points). The digital architecture of the module is detailed and their features in terms of resource consumption and processing speed are illustrated with implementation results into FPGAs from Xilinx. Performance of the solution has been verified with fingerprints from several standard databases that have been acquired with sensors of different sizes and technologies (optical, capacitive, and thermal sweeping).This work was partially funded by Junta de Andalucía under the Project P08-TIC-03674 (with support from the PO FEDER-FSE de Andalucía 2007–2013), by Spanish Ministerio de Economía y Competitividad under the Project TEC2011-24319 (with support fromFEDER), and by the European Community through the MOBY-DIC Project FP7-INFSO-ICT-248858 (http://www.mobydic-project.eu).Peer reviewe