activity
20182024
most citedDeep Learning for Face Recognition: Pride or Prejudiced?

40 citations · 79 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CR20212 cited

Efficient Encrypted Inference on Ensembles of Decision Trees

Kanthi Sarpatwar, Karthik Nandakumar, Nalini Ratha +4

Data privacy concerns often prevent the use of cloud-based machine learning services for sensitive personal data. While homomorphic encryption (HE) offers a potential solution by e…

cs.CR20213 cited

Efficient CNN Building Blocks for Encrypted Data

Nayna Jain, Karthik Nandakumar, Nalini Ratha +2

Machine learning on encrypted data can address the concerns related to privacy and legality of sharing sensitive data with untrustworthy service providers. Fully Homomorphic Encryp…

cs.CR2020

Securing CNN Model and Biometric Template using Blockchain

Akhil Goel, Akshay Agarwal, Mayank Vatsa +2

Blockchain has emerged as a leading technology that ensures security in a distributed framework. Recently, it has been shown that blockchain can be used to convert traditional bloc…

cs.CV201940 cited

Deep Learning for Face Recognition: Pride or Prejudiced?

Shruti Nagpal, Maneet Singh, Richa Singh +1

Do very high accuracies of deep networks suggest pride of effective AI or are deep networks prejudiced? Do they suffer from in-group biases (own-race-bias and own-age-bias), and mi…

cs.CV201934 cited

Diversity in Faces

Michele Merler, Nalini Ratha, Rogerio S. Feris +1

Face recognition is a long standing challenge in the field of Artificial Intelligence (AI). The goal is to create systems that accurately detect, recognize, verify, and understand…

cs.CV2018

Understanding Unequal Gender Classification Accuracy from Face Images

Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha +7

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Acc…