Iris recognition under unconstrained conditions

Amgad A. A. Algawhari; Yongfeng Huang

doi:10.1117/12.2513881

29 October 2018 Iris recognition under unconstrained conditions

Amgad A. A. Algawhari, Yongfeng Huang

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108360D (2018) https://doi.org/10.1117/12.2513881
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

Abstract

This paper studies the iris recognition under unconstrained conditions. In these circumstances iris recognition becomes challenging because of noisy factors such as the off-axis imaging, pose variation, image blurring, illumination change, occlusion, specular highlights and noise. A robust algorithm for localization of non-circular iris boundaries is proposed. It can localize the iris boundaries more accurately than the methods based on the Daugman algorithm. Operating on the filtered iris images, this method determines the outer iris boundaries. First we implemented Canny algorithm for edge detection in the segmented image. Then we ran the edge link algorithm on the edge map, achieving edge lists of connected edge points and selecting the longest one that has maximum number of points for outer iris boundary localization. Finally, we investigated how to extract highly distinctive features in the degraded iris images. We present a sequential forward selection method for seeking a sub-optimal subset of filters from a family of Gabor filters. The recognition performance is greatly improved with a very small number of filters selected. Experiments were conducted on the UBIRIS.v2 iris database and promising results were obtained.

Citation Download Citation

Amgad A. A. Algawhari and Yongfeng Huang "Iris recognition under unconstrained conditions", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360D (29 October 2018); https://doi.org/10.1117/12.2513881

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available