Paper Info
Title
A Deep Learning Approach to Segmentation of Distorted Iris Regions in Head-Mounted Displays
Abstract
In this paper, we consider the next generation of wearable ARlVR display glasses and the challenges of personal authentication on such devices. The use of iris authentication as a mean of creating a seamless biometric link between the user and his personal data offers a viable approach, but due to the likely location of user-facing cameras there are some challenges in achieving an accurate segmentation of the iris. In this paper, a deep neural network was trained to accurately segment distorted iris regions. An appropriate augmentation method is presented to generate the distorted iris dataset used for training from publicly available frontal iris datasets. The proposed method shows promising results in segmenting off-axis iris images in unconstrained conditions.
Year
DOI
Venue
2018
10.1109/GEM.2018.8516446
2018 IEEE Games, Entertainment, Media Conference (GEM)
Keywords
Field
DocType
deep learning approach,head-mounted displays,wearable ARlVR display glasses,personal authentication,iris authentication,personal data,deep neural network,accurately segment distorted iris regions,appropriate augmentation method,distorted iris dataset,frontal iris datasets,biometric link,off-axis iris images
Computer vision,Iris recognition,Wearable computer,Computer science,Segmentation,Image segmentation,Artificial intelligence,Biometrics,Iris flower data set,Deep learning,Artificial neural network
Conference
ISBN
Citations 
PageRank 
978-1-5386-6305-9
0
0.34
References 
Authors
15
3
Name
Order
Citations
PageRank
Viktor Varkarakis141.10
S. Bazrafkan2585.44
P. M. Corcoran341482.56