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Mayank Vatsa
Researcher at Indraprastha Institute of Information Technology
Publications - 178
Citations - 6060
Mayank Vatsa is an academic researcher from Indraprastha Institute of Information Technology. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 44, co-authored 143 publications receiving 5198 citations. Previous affiliations of Mayank Vatsa include Indian Institute of Technology Delhi & University of Sassari.
Papers
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Journal ArticleDOI
Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing
TL;DR: This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition.
Proceedings ArticleDOI
Computationally Efficient Face Spoofing Detection with Motion Magnification
TL;DR: A new approach for spoofing detection in face videos using motion magnification using Eulerian motion magnification approach, which improves the state-of-art performance, especially HOOF descriptor yielding a near perfect half total error rate.
Journal ArticleDOI
Plastic Surgery: A New Dimension to Face Recognition
TL;DR: The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance, so that future face recognition systems will be able to address this important problem.
Proceedings ArticleDOI
Periocular biometrics: When iris recognition fails
TL;DR: A novel algorithm to recognize periocular images in visible spectrum is proposed and the results show promise towards using peroocular region for recognition when the information is not sufficient for iris recognition.
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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.