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Daksha Yadav
Researcher at West Virginia University
Publications - 27
Citations - 899
Daksha Yadav is an academic researcher from West Virginia University. The author has contributed to research in topics: Iris recognition & Facial recognition system. The author has an hindex of 14, co-authored 27 publications receiving 680 citations. Previous affiliations of Daksha Yadav include University of Sassari & Indian Institute of Technology, Jodhpur.
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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.
Proceedings ArticleDOI
Face Presentation Attack with Latex Masks in Multispectral Videos
TL;DR: A unique multispectral video face database for face presentation attack using latex and paper masks and it is observed that the thermal imaging spectrum is most effective in detecting face presentation attacks.
Proceedings ArticleDOI
Revisiting iris recognition with color cosmetic contact lenses
TL;DR: 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.
Proceedings ArticleDOI
LivDet iris 2017 — Iris liveness detection competition 2017
David Yambay,Benedict Becker,Naman Kohli,Daksha Yadav,Adam Czajka,Kevin W. Bowyer,Stephanie Schuckers,Richa Singh,Mayank Vatsa,Afzel Noore,Diego Gragnaniello,Carlo Sansone,Luisa Verdoliva,Lingxiao He,Yiwei Ru,Haiqing Li,Nianfeng Liu,Zhenan Sun,Tieniu Tan +18 more
TL;DR: Results of the third LivDet-Iris 2017 show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect.
Proceedings ArticleDOI
Detecting medley of iris spoofing attacks using DESIST
TL;DR: A novel structural and textural feature based iris spoofing detection framework (DESIST) is proposed which combines multi-order dense Zernike moments and Local Binary Pattern with Variance for representing textural changes in a spoofed iris image.