Scholar
Aditya Nigam
Google Scholar ID: ffSpknUAAAAJ
Associate Professor in SCEE at Indian Institute of Technology Mandi
Citations & Impact
Citations
1,402
H-index
19
i10-index
37
Publications
20
Co-authors
12
list available
Publications
Resume (English only)
Academic Achievements
- Published multiple papers, including but not limited to:
- - 'GaitW: Enhancing Gait Recognition in the Wild Using Dynamic Information' at ACCV'24
- - 'Favoring One Among Equals - Not a Good Idea: Many-to-One Matching for Robust Transformer Based Pedestrian Detection' at WACV'24
- - 'Merry Go Round: Rotate a Frame and Fool a DNN' at CVPR'22
- - 'Anonymizing egocentric videos' at ICCV'21
- - 'Is sharing of egocentric video giving away your biometric signature?' at ECCV'20
- - 'Gait metric learning siamese network exploiting dual of spatio-temporal 3D-CNN intra and LSTM based inter gait-cycle-segment features' at Pattern Recognition Letters'19
- - 'Cancelable knuckle template generation based on LBP-CNN' at ECCV'18
- - 'IHashNet: Iris Hashing Network based on efficient multi-index hashing' at IJCB'20
- - 'Cancelable Iris template generation by aggregating patch level ordinal relations with its holistically extended performance and security analysis' at Image and Vision Computing'20
- - 'Single-Shell to Multi-Shell dMRI Transformation using Spatial and Volumetric Multilevel Hierarchical Reconstruction Framework' at Magnetic Resonance Imaging Journal'22
- - Relevant research results at IEEE EMBC'20.
Research Experience
- Joined the School of Computing and Electrical Engineering (SCEE) at IIT Mandi in August 2014 as a Teaching Fellow. Now serving as an Associate Professor there.
Education
- Received M.Tech and Ph.D. degrees from Indian Institute of Technology, Kanpur in 2009 and 2014 respectively.
Background
- Research interests include Deep Learning, Biometrics, Medical Image Processing & its Analysis, Image Processing, Computer Vision & Machine Learning. Currently working as an Associate Professor at IIT Mandi, passionate about teaching, mainly focusing on Deep Learning and its applications.
