M
Maneet Singh
Researcher at Indraprastha Institute of Information Technology
Publications - 55
Citations - 861
Maneet Singh is an academic researcher from Indraprastha Institute of Information Technology. The author has contributed to research in topics: Facial recognition system & Deep learning. The author has an hindex of 15, co-authored 51 publications receiving 605 citations.
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Learning A Shared Transform Model for Skull to Digital Face Image Matching.
TL;DR: A novel Shared Transform Model is proposed for learning discriminative representations that learns robust features while reducing the intra-class variations between skulls and digital face images, and can assist law enforcement agencies by speeding up the process of skull identification, and reducing the manual load.
Proceedings ArticleDOI
Triplet Transform Learning for Automated Primate Face Recognition
TL;DR: A novel Triplet Transform Learning (TTL) model for learning discriminative representations of primate faces is proposed, where it outperforms the existing approaches and attains state-of-the-art performance on the primates database.
Proceedings ArticleDOI
Region-specific fMRI dictionary for decoding face verification in humans
Daksha Yadav,Naman Kohli,Shruti Nagpal,Maneet Singh,Prateekshit Pandey,Mayank Vatsa,Richa Singh,Afzel Noore,Gokulraj T Prabhakaran,Harsh Mahajan +9 more
TL;DR: A novel two-level fMRI dictionary learning approach to predict if the stimuli observed is genuine or imposter using the brain activation data for selected regions is proposed.
Book ChapterDOI
GroupMixNorm Layer for Learning Fair Models
TL;DR: GroupMixNorm as discussed by the authors proposes a novel in-processing based GroupMixNorm layer for mitigating bias from deep learning models, which probabilistically mixes group-level feature statistics of samples across different groups based on the protected attribute.
Journal ArticleDOI
Detox Loss: Fairness Constraints for Learning With Imbalanced Data
TL;DR: In this article , the authors proposed Detox loss, a bias invariant feature learning loss function for learning unbiased models, which can be used to learn fairer deep learning classifiers, and mitigate bias from existing pre-trained networks, especially in the challenging constraint of imbalanced training data with respect to a protected attribute.