no code implementations • ECCV 2020 • Daksh Thapar, Chetan Arora, Aditya Nigam
In this work, we create a novel kind of privacy attack by extracting the wearer’s gait profile, a well known biometric signature, from such optical flow in the egocentric videos.
no code implementations • 26 Nov 2022 • Britty Baby, Daksh Thapar, Mustafa Chasmai, Tamajit Banerjee, Kunal Dargan, Ashish Suri, Subhashis Banerjee, Chetan Arora
Minimally invasive surgeries and related applications demand surgical tool classification and segmentation at the instance level.
no code implementations • CVPR 2022 • Daksh Thapar, Aditya Nigam, Chetan Arora
On the other hand DNNs are known to be susceptible to Adversarial Attacks (AAs) which add im-perceptible noise to the input.
no code implementations • ICCV 2021 • Daksh Thapar, Aditya Nigam, Chetan Arora
In a more damaging scenario, one can even recognize a wearer using hand gestures from egocentric videos, or identify a wearer in third person videos such as from a surveillance camera.
no code implementations • 11 Nov 2019 • Abhigyan Khaund, Daksh Thapar, Aditya Nigam
We use a Generative Adversarial Network for the task of retrieving the garment that the person in the image was wearing.
no code implementations • 2 Apr 2019 • Daksh Thapar, Gaurav Jaswal, Aditya Nigam
In distinguished experiments, the individual performance of finger, as well as weighted sum score level fusion of major knuckle, minor knuckle, and nail modalities have been computed, justifying our assumption to consider full finger as biometrics instead of its counterparts.
no code implementations • 26 Mar 2019 • Anshul Thakur, Daksh Thapar, Padmanabhan Rajan, Aditya Nigam
Experimental results also confirm the superiority of the triplet loss over the cross-entropy loss in low training data conditions
no code implementations • 15 Dec 2018 • Daksh Thapar, Gaurav Jaswal, Aditya Nigam, Vivek Kanhangad
Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task.
no code implementations • 14 Oct 2017 • Tushar Gupta, Shreyas Malakarjun Patil, Mukkaram Tailor, Daksh Thapar, Aditya Nigam
The segregation of brain fiber tractography data into distinct and anatomically meaningful clusters can help to comprehend the complex brain structure and early investigation and management of various neural disorders.
no code implementations • 13 Oct 2017 • Daksh Thapar, Divyansh Aggarwal, Punjal Agarwal, Aditya Nigam
It is a 2-stage network, in which we have a classification network that initially identifies the viewing point angle.
no code implementations • 26 Sep 2017 • Ranjeet Ranjan Jha, Daksh Thapar, Shreyas Malakarjun Patil, Aditya Nigam
In this paper, we have proposed a novel end-to-end, Unified Biometric ROI Segmentation Network (UBSegNet), for extracting region of interest from five different biometric traits viz.