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

A digital circuit for extracting singular points from fingerprint images

Since singular point extraction plays an important role in many fingerprint recognition systems, a digital circuit to implement such processing is presented herein. A novel algorithm that combines hardware efficiency with precision in the extraction of the points has been developed. The circuit architecture contains three main building blocks to carry out the three main stages of the algorithm: extraction of a partitioned directional image, smoothing, and searching for the patterns associated with singular points. The circuit processes the pixels in a serial way, following a pipeline scheme and executing in parallel several operations. The design flow employed has been supported by CAD tools. It starts with high-level descriptions and ends with the hardware prototyping into a FPGA from Xilinx

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

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-

Microelectronics implementation of directional image-based fuzzy templates for fingerprints

Trabajo presentado al ICM celebrado en El Cairo del 19 al 22 de diciembre de 2010.Fingerprint orientation image, also called directional image, is a widely used method in fingerprint recognition. It helps in classification (accelerating fingerprint identification process) as well as in preprocessing or processing steps (such as fingerprint enhancement or minutiae extraction). Hence, efficient storage of directional image-based information is relevant to achieve low-cost templates not only for “match on card” but also for “authentication on card” solutions. This paper describes how to obtain a fuzzy model to describe the directional image of a fingerprint and how this model can be implemented in hardware efficiently. The CAD tools of the Xfuzzy 3 environment have been employed to accelerate the fuzzy modeling process as well as to implement the directional image-based template into both an FPGA from Xilinx and an ASIC.Peer Reviewe

Aplicación de XFuzzy 3 al procesado de imágenes basado en reglas

Los entornos de desarrollo de sistemas fuzzy se han empleado normalmente para diseñar sistemas de control y de toma de decisiones pero apenas para diseñar sistemas de procesado de imágenes, a pesar de que este campo cuenta ya con numerosas soluciones basadas en Lógica Fuzzy. En este artículo se muestra cómo el entorno Xfuzzy 3 desarrollado en el Instituto de Microelectrónica de Sevilla posee la versatilidad necesaria para abordar el diseño de estos sistemas, facilitando su descripción, verificación, ajuste y síntesis.Ministerio de Ciencia y Tecnología TEC2008-04920Junta de Andalucía P08-TIC-0367

Diseño de sistemas difusos para procesado de imágenes con XFuzzy 3

La presente comunicación describe la utilización de un software de libre distribución, Xfuzzy 3, para ilustrar la aplicación de sistemas difusos al procesamiento de imágenes, en concreto, al problema del aumento de resolución. El proceso de diseño de sistemas difusos quedará cubierto por el uso de las herramientas CAD de descripción, verificación, identificación, aprendizaje y simplificación del entorno XFuzzy en su versión 3.3, que facilitan al alumno la comprensión de todos los pasos del proceso

Microelectronics implementation of directional image-based fuzzy templates for fingerprints

Fingerprint orientation image, also called directional image, is a widely used method in fingerprint recognition. It helps in classification (accelerating fingerprint identification process) as well as in preprocessing or processing steps (such as fingerprint enhancement or minutiae extraction). Hence, efficient storage of directional image-based information is relevant to achieve low-cost templates not only for “match on card” but also for “authentication on card” solutions. This paper describes how to obtain a fuzzy model to describe the directional image of a fingerprint and how this model can be implemented in hardware efficiently. The CAD tools of the Xfuzzy 3 environment have been employed to accelerate the fuzzy modeling process as well as to implement the directional image-based template into both an FPGA from Xilinx and an ASIC

Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone

Nowadays, biometrics based on vein patterns as a trait is a promising technique. Vein patterns satisfy universality, distinctiveness, permanence, performance, and protection against circumvention. However, collectability and acceptability are not completely satisfied. These two properties are directly related to acquisition methods. The acquisition of vein images is usually based on the absorption of near-infrared (NIR) light by the hemoglobin inside the veins, which is higher than in the surrounding tissues. Typically, specific devices are designed to improve the quality of the vein images. However, such devices increase collectability costs and reduce acceptability. This paper focuses on using commercial smartphones with ordinary cameras as potential devices to improve collectability and acceptability. In particular, we use smartphone applications (apps), mainly employed for medical purposes, to acquire images with the smartphone camera and improve the contrast of superficial veins, as if using infrared LEDs. A recognition system has been developed that employs the free IRVeinViewer App to acquire images from wrists and dorsal hands and a feature extraction algorithm based on SIFT (scale-invariant feature transform) with adequate pre- and post-processing stages. The recognition performance has been evaluated with a database composed of 1000 vein images associated to five samples from 20 wrists and 20 dorsal hands, acquired at different times of day, from people of different ages and genders, under five different environmental conditions: day outdoor, indoor with natural light, indoor with natural light and dark homogeneous background, indoor with artificial light, and darkness. The variability of the images acquired in different sessions and under different ambient conditions has a large influence on the recognition rates, such that our results are similar to other systems from the literature that employ specific smartphones and additional light sources. Since reported quality assessment algorithms do not help to reject poorly acquired images, we have evaluated a solution at enrollment and matching that acquires several images subsequently, computes their similarity, and accepts only the samples whose similarity is greater than a threshold. This improves the recognition, and it is practical since our implemented system in Android works in real-time and the usability of the acquisition app is high.MCIN/AEI/ 10.13039/50110001103 Grant PDC2021-121589-I00Fondo Europeo de Desarrollo Regional (FEDER) and Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía Grant US-126514

Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions

Nowadays, there is an increasing number of cameras placed on mobile devices connected to the Internet. Since these cameras acquire and process sensitive and vulnerable data in applications such as surveillance or monitoring, security is essential to avoid cyberattacks. However, cameras on mobile devices have constraints in size, computation and power consumption, so that lightweight security techniques should be considered. Camera identification techniques guarantee the origin of the data. Among the camera identification techniques, Physically Unclonable Functions (PUFs) allow generating unique, distinctive and unpredictable identifiers from the hardware of a device. PUFs are also very suitable to obfuscate secret keys (by binding them to the hardware of the device) and generate random sequences (employed as nonces). In this work, we propose a trusted camera based on PUFs and standard cryptographic algorithms. In addition, a protocol is proposed to protect the communication with the trusted camera, which satisfies authentication, confidentiality, integrity and freshness in the data communication. This is particularly interesting to carry out camera control actions and firmware updates. PUFs from Static Random Access Memories (SRAMs) are selected because cameras typically include SRAMs in its hardware. Therefore, additional hardware is not required and security techniques can be implemented at low cost. Experimental results are shown to prove how the proposed solution can be implemented with the SRAM of commercial Bluetooth Low Energy (BLE) chips included in the communication module of the camera. A proof of concept shows that the proposed solution can be implemented in low-cost cameras.España, Ministerio de Ciencia e Innovación TEC2014-57971-R TEC2017-83557-