356 research outputs found

    A Review: Person Identification using Retinal Fundus Images

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    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\%

    A new method of vascular point detection using artificial neural network

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    Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5×5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database

    A new method of vascular point detection using artificial neural network

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    Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5x5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database

    Review of personal identification systems

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    The growth of the use of biometric personal identification systems has been relatively steady over the last 20 years. The expected biometric revolution which was forecast since the mid 1970\u27s has not yet occurred. The main factor for lower than expected growth has been the cost and user acceptance of the systems. During the last few years, however, a new generation of more reliable, less expensive and better designed biometric devices have come onto the market. This combined with the anticipated expansion of new reliable, user friendly inexpensive systems provides a signal that the revolution is about to begin. This paper provides a glimpse into the future for personal identification systems and focuses on research directions, emerging applications and significant issues of the future

    A pilot study on discriminative power of features of superficial venous pattern in the hand

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    The goal of the project is to develop an automatic way to identify, represent the superficial vasculature of the back hand and investigate its discriminative power as biometric feature. A prototype of a system that extracts the superficial venous pattern of infrared images of back hands will be described. Enhancement algorithms are used to solve the lack of contrast of the infrared images. To trace the veins, a vessel tracking technique is applied, obtaining binary masks of the superficial venous tree. Successively, a method to estimate the blood vessels calibre, length, the location and angles of vessel junctions, will be presented. The discriminative power of these features will be studied, independently and simultaneously, considering two features vector. Pattern matching of two vasculature maps will be performed, to investigate the uniqueness of the vessel network / L’obiettivo del progetto è di sviluppare un metodo automatico per identificare e rappresentare la rete vascolare superficiale presente nel dorso della mano ed investigare sul suo potere discriminativo come caratteristica biometrica. Un prototipo di sistema che estrae l’albero superficiale delle vene da immagini infrarosse del dorso della mano sarà descritto. Algoritmi per il miglioramento del contrasto delle immagini infrarosse saranno applicati. Per tracciare le vene, una tecnica di tracking verrà utilizzata per ottenere una maschera binaria della rete vascolare. Successivamente, un metodo per stimare il calibro e la lunghezza dei vasi sanguigni, la posizione e gli angoli delle giunzioni sarà trattato. Il potere discriminativo delle precedenti caratteristiche verrà studiato ed una tecnica di pattern matching di due modelli vascolari sarà presentata per verificare l’unicità di quest

    Retinal Vasculature Identification and Characterization Using OCT Imaging

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    The eye fundus is the part of the human body where the blood vessels can be directly observed and studied. For this reason, the analysis and diagnosis of many relevant diseases that affect the circulatory system, for example, reference, hypertension, diabetes or arteriosclerosis can be supported by the use of this source of information, analyzing their degree of severity and impact by the study of the properties of the retinal microcirculation. The development of computer aided-diagnosis tools became relevant over the recent years as they support and facilitate the work of specialists, helping to accurately identify the target structures in many processes of analysis and diagnosis. In that sense, the automatic identification of the retinal vasculature is crucial as its manual identification is an exhaustive and tedious work when it is manually performed by the experts. This chapter presents an analysis of the characteristics of the optical coherence tomography imaging and its potential for the retinal vascular identification and characterization. In that sense, we also analyze computational approaches to automatically obtain and characterize the retinal vasculature and provide an intuitive visualization that facilitates the posterior clinical analysis of relevant diseases such as hypertension or diabetes

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

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    [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

    Automatic facial recognition based on facial feature analysis

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    MULTISCALE EDGE DETECTION USING WAVELET MAXIMA FOR IRIS LOCALIZATION

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    Automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications and is regarded as the most reliable and accurate biometric identification system available. Common problems include variations in lighting, poor image quality, noise and interference caused by eyelashes while feature extraction and classification steps rely heavily on the rich textural details of the iris to provide a unique digital signature for an individual. As a result, the stability and integrity of a system depends on effective localization of the iris to generate the iris-code. A new localization method is presented in this paper to undertake these problems. Multiscale edge detection using wavelet maxima is discussed as a preprocessing technique that detects a precise and effective edge for localization and which greatly reduces the search space for the Hough transform, thus improving the overall performance. Linear Hough transform has been used for eyelids isolating, and an adaptive thresholding has been used for eyelashes isolating. A large number of experiments on the CASIA iris database demonstrate the validity and the effectiveness of the proposed approach

    An Efficient Iris Segmentation Technique based on a Multiscale Approach

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    The use of biometric signatures, instead of tokens such as identification cards or computer passwords, continues to gain increasing attention as an efficient means of identification and verification of individuals for controlling access to secured areas, materials, or systems and a wide variety of biometrics has been considered over the years in support of these challenges. Iris recognition is especially attractive due to the stability of the iris texture patterns with age and health conditions. Iris image segmentation and localisation is a key step in iris recognition and plays an essential role the accuracy of matching. In this paper, we propose a new iris segmentation technique using a multiscale approach for edge detection, which is a fundamental issue in image analysis. Due to the presence of speckles, which can be modelled as a a strong multiplicative noise, edge detection for iris segmentation is very important and methods developed so far are generally applied in one single scale. In our proposed method, we introduce the concept of multiscale edge detection to improve iris segmentation. The technique is effecient for edge detetcion, greatly reduces the search space for the Hough transform and at the same time is robust to noise thus improving the overall performance. Linear Hough transform has been used for eyelids isolation, and an adaptive thresholding has been used for isolating eyelashes. Once the iris is segmented, a normalization step has been carried out by converting an iris image from cartesien into polar coordinates which are more suitable to deal with rotation and translation problems. Extensive experiments have been carried out and results obtained have shown an effectiveness of the proposed method which provides a high segmentation success of 99.6%
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