NDLI: An Approach to Iris Contact Lens Detection Based on Deep Image Representations

Content Provider	IEEE Xplore Digital Library
Author	Silva, P. Luz, E. Baeta, R. Menotti, D. Pedrini, H. Falcao, A.X.
Copyright Year	2015
Description	Author affiliation: Comput. Dept., Fed. Univ. of Ouro Preto, Ouro Preto, Brazil (Silva, P.; Luz, E.; Baeta, R.; Menotti, D.) \|\| Inst. of Comput., Univ. of Campinas, Campinas, Brazil (Pedrini, H.; Falcao, A.X.)
Abstract	Spoofing detection is a challenging task in biometric systems, when differentiating illegitimate users from genuine ones. Although iris scans are far more inclusive than fingerprints, and also more precise for person authentication, iris recognition systems are vulnerable to spoofing via textured cosmetic contact lenses. Iris spoofing detection is also referred to as liveness detection (binary classification of fake and real images). In this work, we focus on a three-class detection problem: images with textured (colored) contact lenses, soft contact lenses, and no lenses. Our approach uses a convolutional network to build a deep image representation and an additional fully-connected single layer with soft max regression for classification. Experiments are conducted in comparison with a state-of-the-art approach (SOTA) on two public iris image databases for contact lens detection: 2013 Notre Dame and IIIT-Delhi. Our approach can achieve a 30% performance gain over SOTA on the former database (from 80% to 86%) and comparable results on the latter. Since IIIT-Delhi does not provide segmented iris images and, differently from SOTA, our approach does not segment the iris yet, we conclude that these are very promising results.
Starting Page	157
Ending Page	164
File Size	1647498
Page Count	8
File Format	PDF
ISSN	15301834
e-ISBN	9781467379625
DOI	10.1109/SIBGRAPI.2015.16
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-26
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lenses Iris recognition Databases Iris Training Network topology Image representation Convolutional Networks Iris Biometrics Contact Lens Detection Deep Learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Detection of Contact-Lens-Based Iris Biometric Spoofs Using Stereo Imaging

Unraveling the Effect of Textured Contact Lenses on Iris Recognition

Contact Lens Detection Based on Weighted LBP

Variation in accuracy of textured contact lens detection based on sensor and lens pattern

Robust Detection of Textured Contact Lenses in Iris Recognition Using BSIF

Development of deep learning-based facial expression recognition system

Novel Algorithm to Classify Iris Image Based on Entropy by Using Neural Network

Image recognition based on deep learning

Iris liveness detection methods in the mobile biometrics scenario

An Approach to Iris Contact Lens Detection Based on Deep Image Representations

Similar Documents

Detection of Contact-Lens-Based Iris Biometric Spoofs Using Stereo Imaging

Unraveling the Effect of Textured Contact Lenses on Iris Recognition

Contact Lens Detection Based on Weighted LBP

Variation in accuracy of textured contact lens detection based on sensor and lens pattern

Robust Detection of Textured Contact Lenses in Iris Recognition Using BSIF

Development of deep learning-based facial expression recognition system

Novel Algorithm to Classify Iris Image Based on Entropy by Using Neural Network

Image recognition based on deep learning

Iris liveness detection methods in the mobile biometrics scenario

An Approach to Iris Contact Lens Detection Based on Deep Image Representations