R
Richa Singh
Researcher at Indian Institute of Technology, Jodhpur
Publications - 493
Citations - 11353
Richa Singh is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Facial recognition system & Deep learning. The author has an hindex of 53, co-authored 422 publications receiving 9145 citations. Previous affiliations of Richa Singh include Indian Institutes of Technology & University of Virginia.
Papers
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Proceedings Article
Unravelling Robustness of Deep Learning Based Face Recognition Against Adversarial Attacks
TL;DR: In this article, the authors investigated the impact of adversarial attacks on the robustness of DNN-based face recognition models and proposed several effective countermeasures to mitigate the impact.
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On cross spectral periocular recognition
TL;DR: The proposed combined neural network architecture, on the cross spectral database, shows improved performance compared to existing feature descriptors and cross domain algorithms.
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Face anti-spoofing with multifeature videolet aggregation
Talha Ahmad Siddiqui,Samarth Bharadwaj,Tejas I. Dhamecha,Akshay Agarwal,Mayank Vatsa,Richa Singh,Nalini K. Ratha +6 more
TL;DR: A novel multi-feature evidence aggregation method for face spoofing detection that fuses evidence from features encoding of both texture and motion properties in the face and also the surrounding scene regions and provides robustness to different attacks.
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Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition
TL;DR: This paper attempts to unravel three aspects related to the robustness of DNNs for face recognition in terms of vulnerabilities to attacks, detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and making corrections to the processing pipeline to alleviate the problem.
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A Mosaicing Scheme for Pose-Invariant Face Recognition
TL;DR: A face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semiproflle face images of an individual is described.