Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46947
Title: Multi-angle based lively sclera biometrics at a distance
Authors: Das, Abhijit
Pal, Umapada
Ballester, Miguel Angel Ferrer 
Blumenstein, Michael
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Iris recognition
Image segmentation
Feature extraction
biometrics (access control)
Issue Date: 2015
Journal: IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM
Conference: 2014 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2014 - 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, CIBIM 2014 
Abstract: This piece of work proposes a liveliness based sclera eye biometric, validation and recognition technique at a distance. The images in this work are acquired by a digital camera in the visible spectrum at varying distance of about 1 meter from the individual. Each individual during registration as well as validation is asked to look straight and move their eye ball up, left and right keeping their face straight to incorporate liveliness of the data. At first the image is divided vertically into two halves and the eyes are detected in each half of the face image that is captured, by locating the eye ball by a Circular Hough Transform. Then the eye image is cropped out automatically using the radius of the iris. Next a C-means-based segmentation is used for sclera segmentation followed by vessel enhancement by the adaptive histogram equalization and Haar filtering. The feature extraction was performed by patch-based Dense-LDP (Linear Directive Pattern). Furthermore each training image is used to form a bag of features, which is used to produce the training model. Each of the images of the different poses is combined at the feature level and the image level to obtain higher accuracy and to incorporate liveliness. The fusion that produces the best result is considered. Support Vector Machines (SVMs) are used for classification. Here images from 82 individuals (both left and right eye i.e. 164 different eyes) are used and an appreciable Equal Error Rate of 0.52% is achieved in this work.
URI: http://hdl.handle.net/10553/46947
ISBN: 9781479945344
ISSN: 2325-4300
DOI: 10.1109/CIBIM.2014.7015439
Source: IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM[ISSN 2325-4300],v. 2015-January (7015439), p. 22-29
Appears in Collections:Actas de congresos
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