9 citations · 23 across the 6 of their papers we have counts for
8 papers
EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images
Prithviraj Dhar, Amit Kumar, Kirsten Kaplan +3
A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency…
Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation
Prithviraj Dhar, Joshua Gleason, Aniket Roy +3
Face recognition networks generally demonstrate bias with respect to sensitive attributes like gender, skintone etc. For gender and skintone, we observe that the regions of the fac…
PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition
Prithviraj Dhar, Joshua Gleason, Aniket Roy +2
Face recognition networks encode information about sensitive attributes while being trained for identity classification. Such encoding has two major issues: (a) it makes the face r…
Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition
Prithviraj Dhar, Joshua Gleason, Hossein Souri +2
State-of-the-art deep networks implicitly encode gender information while being trained for face recognition. Gender is often viewed as an important attribute with respect to ident…
Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined
Connor J. Parde, Y. Ivette Colón, Matthew Q. Hill +3
Deep convolutional neural networks (DCNNs) trained for face identification develop representations that generalize over variable images, while retaining subject (e.g., gender) and…
How are attributes expressed in face DCNNs?
Prithviraj Dhar, Ankan Bansal, Carlos D. Castillo +3
As deep networks become increasingly accurate at recognizing faces, it is vital to understand how these networks process faces. While these networks are solely trained to recognize…