ADAPTABLE FINGERPRINT MINUTIAE EXTRACTION ALGORITHM BASED-ON CROSSING NUMBER METHOD FOR HARDWARE IMPLEMENTATION USING FPGA DEVICE

In this article. a main perspective of developing and implementing fingerprint extraction and matching algorithms as a pari of fingerprint recognition system is focused. First, developing a simple algorithm to extract fingerprint features and test this algorithm on Pc. The second thing is implementing this algorithm into FPGA devices. The major research topics on which the proposed approach is developing and modifying fingerprint extraction feature algorithm. This development and modification are using crossing number method on pixel representation value '0'. In this new proposed algorithm, it is no need a process concerning ROI segmentation and no trigonometry calculation. And specially in obtaining their parameters using Angle Calculation Block avoiding floating points calculation. As this method is local feature that usually involve with 60-100 minutiae points, makes the template is small in size. Providing FAR. FRR and EER, performs the performance evaluation of proposed algorithm. The result is an adaptable fingerprint minutiae extraction algorithm into hardware implementation with 14.05 % of EEl?, better than reference algorithm, which is 20.39 % . The computational time is 18 seconds less than a similar method, which takes 60-90 seconds just for pre-processing step. The first step of algorithm implementation in hardware environment (embedded) using FPGA Device by developing IP Core without using any soft processor is presented

Digital implementation of the cellular sensor-computers

Two different kinds of cellular sensor-processor architectures are used nowadays in various applications. The first is the traditional sensor-processor architecture, where the sensor and the processor arrays are mapped into each other. The second is the foveal architecture, in which a small active fovea is navigating in a large sensor array. This second architecture is introduced and compared here. Both of these architectures can be implemented with analog and digital processor arrays. The efficiency of the different implementation types, depending on the used CMOS technology, is analyzed. It turned out, that the finer the technology is, the better to use digital implementation rather than analog

Biblioteca de procesamiento de imágenes optimizada para Arm Cortex-M7

La mayoría de los vehículos en la actualidad están equipados con sistemas que asisten al conductor en tareas difíciles y repetitivas, como reducir la velocidad del vehículo en una zona escolar. Algunos de estos sistemas requieren una computadora a bordo capaz de realizar el procesamiento en tiempo real de las imágenes del camino obtenidas por una cámara. El objetivo de este proyecto es implementar una librería de procesamiento de imagen optimizada para la arquitectura ARM® Cortex®-M7. Esta librería provee rutinas para realizar filtrado espacial, resta, binarización y extracción de la información direccional de una imagen, así como el reconocimiento parametrizado de patrones de una figura predefinida utilizando la Transformada Generalizada de Hough. Estas rutinas están escritas en el lenguaje de programación C, para aprovechar las optimizaciones del compilador GNU ARM C, y obtener el máximo desempeño y el mínimo tamaño de objetos. El desempeño de las rutinas fue comparado con la implementación existente para otro microcontrolador, el Freescale® MPC5561. Para probar la funcionalidad de esta librería en una aplicación de tiempo real, se desarrolló un sistema de reconocimiento de señales de tráfico. Los resultados muestran que en promedio el tiempo de ejecución es 18% más rápido y el tamaño de objetos es 25% menor que en la implementación de referencia, lo que habilita a este sistema para procesar hasta 24 cuadros por segundo. En conclusión, estos resultados demuestran la funcionalidad de la librería de procesamiento de imágenes en sistemas de tiempo real.Most modern vehicles are equipped with systems that assist the driver by automating difficult and repetitive tasks, such as reducing the vehicle speed in a school zone. Some of these systems require an onboard computer capable of performing real-time processing of the road images captured by a camera. The goal of this project is to implement an optimized image processing library for the ARM® Cortex®-M7 architecture. This library includes the routines to perform image spatial filtering, subtraction, binarization, and extraction of the directional information along with the parameterized pattern recognition of a predefined template using the Generalized Hough Transform (GHT). These routines are written in the C programming language, leveraging GNU ARM C compiler optimizations to obtain maximum performance and minimum object size. The performance of the routines was benchmarked with an existing implementation for a different microcontroller, the Freescale® MPC5561. To prove the usability of this library in a real-time application, a Traffic Sign Recognition (TSR) system was implemented. The results show that in average the execution time is 18% faster and the binary object size is 25% smaller than in the reference implementation, enabling the TSR application to process up to 24 fps. In conclusion, these results demonstrate that the image processing library implemented in this project is suitable for real-time applications.ITESO, A. C.Consejo Nacional de Ciencia y TecnologíaContinental Automotiv

An Embedded Biometric Sensor for Ubiquitous Authentication

Communication networks and distributed technologies move people towards the era of ubiquitous computing. An ubiquitous environment needs many authentication sensors for users recognition, in order to provide a secure infrastructure for both user access to resources and services and information management. Today the security requirements must ensure secure and trusted user information to protect sensitive data resource access and they could be used for user traceability inside the platform. Conventional authentication systems, based on username and password, are in crisis since they are not able to guarantee a suitable security level for several applications. Biometric authentication systems represent a valid alternative to the conventional authentication systems providing a flexible einfrastructure towards an integrated solution supporting the requirement for improved inter-organizational functionality. In this work the study and the implementation of a fingerprintsbased embedded biometric system is proposed. Typical strategies implemented in Identity Management Systems could be useful to protect biometric information. The proposed sensor can be seen as a self-contained sensor: it performs the all elaboration steps on board, a necessary requisite to strengthen security, so that sensible data are securely managed and stored inside the sensor, without any data leaking out. The sensor has been prototyped via an FPGA-based platform achieving fast execution time and a good final throughput. Resources used, elaboration times of the sensor are reported. Finally, recognition rates of the proposed embedded biometric sensor have been evaluated considering three different databases: the FVC2002 reference database, the CSAI/Biometrika proprietary database, and the CSAI/Secugen proprietary database. The best achieved FAR and FRR indexes are respectively 1.07% and 8.33%, with an elaboration time of 183.32 ms and a working frequency of 22.5 MHz

A Multimodal Technique for an Embedded Fingerprint Recognizer in Mobile Payment Systems

The development and the diffusion of distributed systems, directly connected to recent communication technologies, move people towards the era of mobile and ubiquitous systems. Distributed systems make merchant-customer relationships closer and more flexible, using reliable e-commerce technologies. These systems and environments need many distributed access points, for the creation and management of secure identities and for the secure recognition of users. Traditionally, these access points can be made possible by a software system with a main central server. This work proposes the study and implementation of a multimodal technique, based on biometric information, for identity management and personal ubiquitous authentication. The multimodal technique uses both fingerprint micro features (minutiae) and fingerprint macro features (singularity points) for robust user authentication. To strengthen the security level of electronic payment systems, an embedded hardware prototype has been also created: acting as self-contained sensors, it performs the entire authentication process on the same device, so that all critical information (e.g. biometric data, account transactions and cryptographic keys), are managed and stored inside the sensor, without any data transmission. The sensor has been prototyped using the Celoxica RC203E board, achieving fast execution time, low working frequency, and good recognition performance