Proceedings ArticleDOI
Revisiting iris recognition with color cosmetic contact lenses
Naman Kohli,Daksha Yadav,Mayank Vatsa,Richa Singh +3 more
- pp 1-7
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TLDR
An in-depth analysis of the effect of contact lens on iris recognition performance is presented and the results computed using VeriEye suggest that color cosmetic lens significantly increases the false rejection at a fixed false acceptance rate.Abstract:
Over the years, iris recognition has gained importance in the biometrics applications and is being used in several large scale nationwide projects. Though iris patterns are unique, they may be affected by external factors such as illumination, camera-eye angle, and sensor interoperability. The presence of contact lens, particularly color cosmetic lens, may also pose a challenge to iris biometrics as it obfuscates the iris patterns and changes the inter and intra-class distributions. This paper presents an in-depth analysis of the effect of contact lens on iris recognition performance. We also present the IIIT-D Contact Lens Iris database with over 6500 images pertaining to 101 subjects. For each subject, images are captured without lens, transparent (prescription) lens, and color cosmetic lens (textured) using two different iris sensors. The results computed using VeriEye suggest that color cosmetic lens significantly increases the false rejection at a fixed false acceptance rate. Also, the experiments on four existing lens detection algorithms suggest that incorporating lens detection helps in maintaining the iris recognition performance. However further research is required to build sophisticated lens detection algorithm that can improve iris recognition.read more
Citations
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Journal ArticleDOI
Deep Representations for Iris, Face, and Fingerprint Spoofing Detection
David Menotti,Giovani Chiachia,Allan Pinto,William Robson Schwartz,Helio Pedrini,Alexandre X. Falcão,Anderson Rocha +6 more
TL;DR: This work assumes a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches based on convolutional networks.
Journal ArticleDOI
Deep Representations for Iris, Face, and Fingerprint Spoofing Detection
David Menotti,Giovani Chiachia,Allan Pinto,William Robson Schwartz,Helio Pedrini,Alexandre X. Falcão,Anderson Rocha +6 more
TL;DR: In this paper, the authors proposed two deep learning approaches for spoofing detection of iris, face, and fingerprint modalities based on a very limited knowledge about biometric spoofing at the sensor.
Journal ArticleDOI
Unraveling the Effect of Textured Contact Lenses on Iris Recognition
TL;DR: This paper presents a novel lens detection algorithm that can be used to reduce the effect of contact lenses and outperforms other lens detection algorithms on the two databases and shows improved iris recognition performance.
Journal ArticleDOI
Detecting Silicone Mask-Based Presentation Attack via Deep Dictionary Learning
TL;DR: This paper introduces the first-of-its-kind silicone mask attack database which contains 130 real and attacked videos to facilitate research in developing presentation attack detection algorithms for this challenging scenario.
Journal ArticleDOI
Ocular biometrics
TL;DR: A path forward is proposed to advance the research on ocular recognition by improving the sensing technology, heterogeneous recognition for addressing interoperability, utilizing advanced machine learning algorithms for better representation and classification, and developing algorithms for ocular Recognition at a distance.
References
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Proceedings ArticleDOI
On iris camera interoperability
TL;DR: This work proposes an iris camera classification-based preprocessing framework to address iris interoperability and shows a significant improvement in the cross-camera iris recognition accuracy using the proposed approach.
Proceedings ArticleDOI
Detection of Iris Texture Distortions By Analyzing Iris Code Matching Results
S. Ring,Kevin W. Bowyer +1 more
TL;DR: This approach assumes that some local distortions of the iris texture are not detected at the segmentation stage, and that these generate corresponding regions of local distortion in the iri code derived from the image.
Proceedings ArticleDOI
Contact lenses: Handle with care for iris recognition
TL;DR: This is the first work that is aware of to look at the effects of prescription contact lenses on iris biometrics, and shows that contacts lens wearers are 14 times more likely to be falsely rejected by the IrisBEE iris recognition system at a Hamming distance threshold than non contact lens wearer.