9 citations · 19 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
On measuring the iconicity of a face
Prithviraj Dhar, Carlos D. Castillo, Rama Chellappa
For a given identity in a face dataset, there are certain iconic images which are more representative of the subject than others. In this paper, we explore the problem of computing…
Learning without Memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng +2
Incremental learning (IL) is an important task aimed at increasing the capability of a trained model, in terms of the number of classes recognizable by the model. The key problem i…