Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Automatic Pixel-Parallel Extraction of the Retinal Vascular Tree Algorithm Design, On-Chip Implementation an Applications

[Resumen] La tesis doctoral propone un nuevo algoritmo para la extracción del árbol arterio-venoso en imágenes digitales de retina usando sistemas pixel paralelo que le confiere un procesamiento a alta velocidad, Inicialmente el problema de la extracción del árbol arterio venoso se estudió desde el punto de vista del procesamiento de imágenes utilizando técnicas pixel paralelo, concretamente bajo el paradigma de las Cellular Neural Networks. Este algoritmo utiliza una técnica de contornos activos, los Pixel level snakes (PLS) que permiten aprovechar las ventajas de los contornos activos, como es su capacidad de funcionamiento con contornos borrosos así como su robustez ante el ruido, y al mismo tiempo todo ello procesándose a una alta velocidad de computación. Esta técnica permite también su proyección en un dispositivo hardware específico. La primera versión del algoritmo fue diseñada basándose en el paradigma CNN. Los resultados obtenidos eran buenos bajo el punto de vista del procesado de imagen. Sin embargo, la complejidad de algunas de las operaciones propuestas en esta versión eran de una alta complejidad para ser implementados en los chips pixel paralelos actuales con capacidades SIMD (Single Instruction Multiple Data). Esta versión ha sido redefinida para ser implementada en un chip SIMD. Esta última versión ha sido analizada desde un punto de vista del ajuste de los resultados y desde el punto de vista de la velocidad de ejecución. Para el primer análisis se ha hecho uso de una base de datos pública, concretamente la DRIVE (Digital Retinal Image for Vessel Extraction). Para el análisis de los tiempos de ejecución, se implementó el algoritmo en un chip específico, el SCAMP-3 vision system. El análisis de ambos aspectos ha permitido observar, que el ajuste obtenido sobre los resultados es alto, aunque existen algoritmos con un ajuste mejor, y el tiempo de ejecución es realmente rápido y no existe ningún algoritmo en la bibliografía que mejore el tiempo obtenido con la implementación propuesta en esta tesis. Asimismo se ha realizado un estudio de la mejora que se podría obtener utilizando una técnica de solapamiento, puesto que debido a la alta resolución de las imágenes utilizadas, estas se han tenido que dividir en subventanas para su procesamiento. Este análisis ha demostrado que la mejora obtenida es mínima en comparación con el notable incremento del tiempo de ejecución, siendo descartada su utilización. Una vez demostrado el funcionamiento del algoritmo se ha procedido a su inclusión en aplicaciones prácticas que se encontraban ya funcionando utilizando algoritmos clásicos para la extracción del árbol arterio venoso. Las aplicaciones corresponden a dos ámbitos diferentes con necesidades propias, el ámbito médico y la autenticación de personas. Para la autenticación de personas se observó que el funcionamiento es igual que usando las versiones clásicas, manteniendo un 100% de efectividad en la identificación de personas. En el caso de la aplicación médica, se incluyó dentro de un sistema de estimación del índice arterio-venoso, mostrando un funcionamiento con valores similares

Retinal blood vessel segmentation: methods and implementations

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation. The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation. The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel. The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition. Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation

Retinal blood vessel segmentation: methods and implementations

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation. The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation. The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel. The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition. Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation

Retinal blood vessel segmentation: methods and implementations

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation. The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation. The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel. The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition. Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation

A Review: Person Identification using Retinal Fundus Images

In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100\% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90\%

Automatic system for personal authentication using the retinal vessel tree as biometric pattern

[Resumen] La autenticación fiable de personas es un servicio cuya demanda aumenta en muchos campos, no sólo en entornos policiales o militares sino también en aplicaciones civiles tales como el control de acceso a zonas restringidas o la gestión de transacciones nancieras. Los sistemas de autenticación tradicionales están basados en el conocimiento (una palabra clave o un PIN ) o en la posesión (una tarjeta, o una llave). Dichos sistemas no son su cientemente ables en numerosos entornos, debido a su incapacidad común para diferenciar entre un usuario verdaderamente autorizado y otro que fraudulentamente haya adquirido el privilegio. Una solución para estos problemas se encuentra en las tecnologías de autenticación basadas en biometría. Un sistema biométrico es un sistema de reconocimiento de patrones que establece la autenticidad de los individuos caracterizándolos por medio de alguna característica física o de comportamiento. Existen muchas tecnologías de autenticación, algunas de ellas ya implementadas en paquetes comerciales. Las técnicas biométricas más comunes son la huella digital, probablemente la característica más antigua usada en biometría, iris, cara, geometría de la mano y, en cuanto a las características de comportamiento, reconocimiento de voz y rma. Hoy en día, la mayoría de los esfuerzos en los sistemas biométricos van encaminados al diseño de entornos más xi xii seguros donde sea más difícil, o virtualmente imposible, crear una copia de las propiedades utilizadas en el sistema para discriminar entre usuarios autorizados y no autorizados. En este contexto, el patrón de vasos sanguíneos en la retina se presenta como una característica biométrica relativamente joven pero muy interesante debido a sus propiedades inherentes. La más importante es que se trata de un patrón único para cada individuo. Además, al ser una característica interna es casi imposible crear una copia falsa. Por último, otra propiedad interesante es que el patrón no cambia signi cativamente a lo largo del tiempo excepto en casos de algunas patologías serias y no muy comunes. Por todo ello, el patrón de retina puede ser considerado un rasgo biométrico válido para la autenticación personal ya que es único, invariante en el tiempo y casi imposible de imitar. Por otra parte, el mayor incoveniente en el uso del patrón de vasos de la retina como característica biométrica radica en la etapa de adquisición todav ía percibida por el usuario como invasiva e incómoda. Hoy en día, existen mecanismos para obtener imágenes digitales de manera instantánea a través de cámaras no invasivas pero estos avances requieren a su vez una mayor tolerancia a variaciones en la calidad de la imagen adquirida y, por tanto, métodos computacionales más elaborados que sean capaces de procesar la información en entornos más heterogéneos. En esta tesis se presenta un nuevo sistema de autenticación automático usando el árbol retiniano como característica biométrica. El objetivo es diseñar y desarrollar un patrón biométrico robusto y compacto que sea fácilmente manejable y almacenable en dispositivos móviles de hoy en día como tarjetas con chip. La plantilla biométrica desarrollada a partir del árbol retiniano consiste en sus puntos característicos (bifurcaciones y cruces entre vasos) de forma que no sea necesario el almacenamiento y procesado de todo el árbol para realizar la autenticación

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers