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Aakarsh Malhotra
Researcher at Indraprastha Institute of Information Technology
Publications - 19
Citations - 233
Aakarsh Malhotra is an academic researcher from Indraprastha Institute of Information Technology. The author has contributed to research in topics: Computer science & Authentication. The author has an hindex of 5, co-authored 14 publications receiving 112 citations. Previous affiliations of Aakarsh Malhotra include Indian Institute of Technology Dhanbad.
Papers
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Proceedings ArticleDOI
On smartphone camera based fingerphoto authentication
TL;DR: A novel ScatNet feature based fingerphoto matching approach is proposed to aid the matching process and to attenuate the effect of capture variations, and results show improved performance across multiple challenges present in the database.
Proceedings ArticleDOI
Multimodal biometric recognition for toddlers and pre-school children
TL;DR: This research is the first of its kind attempt to prepare a multimodal biometric database for toddlers and pre-school children, and suggests that while iris is highly accurate, it requires constant adult supervision to attain cooperation from children.
Posted Content
Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images
Aakarsh Malhotra,Surbhi Mittal,Puspita Majumdar,Saheb Chhabra,Kartik Thakral,Mayank Vatsa,Richa Singh,Santanu Chaudhury,Ashwin Pudrod,Anjali Agrawal +9 more
TL;DR: The proposed network not only predicts whether the CXR has COVID-19 features present or not, it also performs semantic segmentation of the regions of interest to make the model explainable.
Journal ArticleDOI
Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images.
Aakarsh Malhotra,Brachet, Julien,Surbhi Mittal,Puspita Majumdar,Saheb Chhabra,Kartik Thakral,Mayank Vatsa,Richa Singh,Santanu Chaudhury,Ashwin Pudrod,Anjali Agrawal +10 more
TL;DR: In this article, the authors presented the COVID-19 Multi-Task Network (COMiT-Net) which is an automated end-to-end network for COVID19 screening.
Journal ArticleDOI
On Matching Finger-Selfies Using Deep Scattering Networks
TL;DR: An algorithm which comprises segmentation, enhancement, Deep Scattering Network based feature extraction, and Random Decision Forest to authenticate finger-selfies is proposed and results and comparison with existing algorithms show the efficacy of the proposed algorithm.