Search Results for author: Prithviraj Dhar

Found 8 papers, 1 papers with code

EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images

no code implementations22 Dec 2021 Prithviraj Dhar, Amit Kumar, Kirsten Kaplan, Khushi Gupta, Rakesh Ranjan, Rama Chellappa

To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting.

PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition

no code implementations ICCV 2021 Prithviraj Dhar, Joshua Gleason, Aniket Roy, Carlos D. Castillo, Rama Chellappa

We show the efficacy of PASS to reduce gender and skintone information in descriptors from SOTA face recognition networks like Arcface.

Face Recognition

Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition

no code implementations14 Jun 2020 Prithviraj Dhar, Joshua Gleason, Hossein Souri, Carlos D. Castillo, Rama Chellappa

Therefore, we present a novel `Adversarial Gender De-biasing algorithm (AGENDA)' to reduce the gender information present in face descriptors obtained from previously trained face recognition networks.

Face Recognition Face Verification +1

How are attributes expressed in face DCNNs?

no code implementations12 Oct 2019 Prithviraj Dhar, Ankan Bansal, Carlos D. Castillo, Joshua Gleason, P. Jonathon Phillips, Rama Chellappa

In the final fully connected layer of the networks, we found the order of expressivity for facial attributes to be Age > Sex > Yaw.

On measuring the iconicity of a face

no code implementations4 Mar 2019 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.

Face Verification

Learning without Memorizing

1 code implementation CVPR 2019 Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu, Rama Chellappa

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.

Incremental Learning

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