5,724 research outputs found
Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud
Biometric recognition, or simply biometrics, is the use of biological
attributes such as face, fingerprints or iris in order to recognize an
individual in an automated manner. A key application of biometrics is
authentication; i.e., using said biological attributes to provide access by
verifying the claimed identity of an individual. This paper presents a
framework for Biometrics-as-a-Service (BaaS) that performs biometric matching
operations in the cloud, while relying on simple and ubiquitous consumer
devices such as smartphones. Further, the framework promotes innovation by
providing interfaces for a plurality of software developers to upload their
matching algorithms to the cloud. When a biometric authentication request is
submitted, the system uses a criteria to automatically select an appropriate
matching algorithm. Every time a particular algorithm is selected, the
corresponding developer is rendered a micropayment. This creates an innovative
and competitive ecosystem that benefits both software developers and the
consumers. As a case study, we have implemented the following: (a) an ocular
recognition system using a mobile web interface providing user access to a
biometric authentication service, and (b) a Linux-based virtual machine
environment used by software developers for algorithm development and
submission
Comparing the legendre wavelet filter and the gabor wavelet filter for feature extraction based on iris recognition system
Iris recognition system is today among the most
reliable form of biometric recognition. Some of the reasons why
the iris recognition system is reliable include; Iris never changes
due to ageing and individual can be recognized with their irises
from long distances up to 50m away. The iris recognition system
process includes four main steps. The four main steps are; iris
image acquisition, preprocessing, feature extraction and
matching, which makes the processes in recognizing an
individual with his or her iris. However, most researchers
recognized feature extraction as a critical stage in the
recognition process. The stage is tasked with extracting unique
feature of the individual to be recognized. Different algorithm
over two-decade has been proposed to extract features from the
iris. This research considered the Gabor filter, which is one of
the most used and Legendre wavelet filters. We also apply them
on three different datasets; CASIA, UBIRIS and MMU
databases. Then we evaluate and compare based on the False
Acceptance Rate (FAR), False Rejection Rate (FRR), Genuine
Acceptance Rate (GAR) and their accuracy. The result shows a
significate increase in recognition accuracy of the Legendre
wavelet filter against the Gabor filter with up to 5.4% difference
when applied with the UBIRIS database
An Improved Algorithm for Eye Corner Detection
In this paper, a modified algorithm for the detection of nasal and temporal
eye corners is presented. The algorithm is a modification of the Santos and
Proenka Method. In the first step, we detect the face and the eyes using
classifiers based on Haar-like features. We then segment out the sclera, from
the detected eye region. From the segmented sclera, we segment out an
approximate eyelid contour. Eye corner candidates are obtained using Harris and
Stephens corner detector. We introduce a post-pruning of the Eye corner
candidates to locate the eye corners, finally. The algorithm has been tested on
Yale, JAFFE databases as well as our created database
Evaluating the impact of image preprocessing on iris segmentation
La segmentación del iris es una de las etapas más importantes en los sistemas de reconocimiento del iris. En este trabajo se aplican algoritmos de preprocesamiento de la imagen con el objetivo de evaluar su impacto en los porcentajes de segmentación exitosa del iris. Los algoritmos utilizados se basan en el ajuste del histograma, filtros Gaussianos y en la eliminación del reflejo especular en imágenes del ojo humano. Se aplica el método de segmentación introducido por Masek a 199 imágenes tomadas bajo condiciones no controladas, pertenecientes a la base de datos CASIA-irisV3, antes y después de aplicar los algoritmos de preprocesamiento. Posteriormente se evalúa el impacto de los algoritmos de preprocesamiento en el porcentaje de segmentación exitosa del iris por medio de una inspección visual de las imágenes, para determinar si las circunferencias detectadas del iris y de la pupila corresponden adecuadamente con el iris y la pupila de la imagen real. El algoritmo que generó uno de los mayores incrementos de los porcentajes de segmentación exitosa (pasa de 59% a 73%) es aquel que combina la eliminación de reflejos especulares, seguido por la aplicación de un filtro Gaussiano con máscara 5x5. Los resultados obtenidos señalan la importancia de una etapa previa de preprocesamiento de la imagen como paso previo para garantizar una mayor efectividad en el proceso de detección de bordes y segmentación del iris.Segmentation is one of the most important stages in iris recognition systems. In this paper, image preprocessing algorithms are applied in order to evaluate their impact on successful iris segmentation. The preprocessing algorithms are based on histogram adjustment, Gaussian filters and suppression of specular reflections in human eye images. The segmentation method introduced by Masek is applied on 199 images acquired under unconstrained conditions, belonging to the CASIA-irisV3 database, before and after applying the preprocessing algorithms. Then, the impact of image preprocessing algorithms on the percentage of successful iris segmentation is evaluated by means of a visual inspection of images in order to determine if circumferences of iris and pupil were detected correctly. An increase from 59% to 73% in percentage of successful iris segmentation is obtained with an algorithm that combine elimination of specular reflections, followed by the implementation of a Gaussian filter having a 5x5 kernel. The results highlight the importance of a preprocessing stage as a previous step in order to improve the performance during the edge detection and iris segmentation processes
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