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Found 11 papers, 0 papers with code
Is Sharing of Egocentric Video Giving Away Your Biometric Signature?
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.
From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments
Minimally invasive surgeries and related applications demand surgical tool classification and segmentation at the instance level.
Merry Go Round: Rotate a Frame and Fool a DNN
On the other hand DNNs are known to be susceptible to Adversarial Attacks (AAs) which add im-perceptible noise to the input.
Anonymizing Egocentric Videos
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.
PoshakNet: Framework for matching dresses from real-life photos using GAN and Siamese Network
We use a Generative Adversarial Network for the task of retrieving the garment that the person in the image was wearing.
FKIMNet: A Finger Dorsal Image Matching Network Comparing Component (Major, Minor and Nail) Matching with Holistic (Finger Dorsal) Matching
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.
Multiscale CNN based Deep Metric Learning for Bioacoustic Classification: Overcoming Training Data Scarcity Using Dynamic Triplet Loss
Experimental results also confirm the superiority of the triplet loss over the cross-entropy loss in low training data conditions
PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features
Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task.
BrainSegNet : A Segmentation Network for Human Brain Fiber Tractography Data into Anatomically Meaningful Clusters
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.
VGR-Net: A View Invariant Gait Recognition Network
It is a 2-stage network, in which we have a classification network that initially identifies the viewing point angle.
UBSegNet: Unified Biometric Region of Interest Segmentation Network
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.
