activity
20192021
most citedTowards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

21 citations · 22 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV202121 cited

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

Viktor Varkarakis, Wang Yao, Peter Corcoran

The recent availability of low-power neural accelerator hardware, combined with improvements in end-to-end neural facial recognition algorithms provides, enabling technology for on…

cs.NE2020

Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets

Viktor Varkarakis, Shabab Bazrafkan, Peter Corcoran

StyleGAN is a state-of-art generative adversarial network architecture that generates random 2D high-quality synthetic facial data samples. In this paper, we recap the StyleGAN arc…

cs.CV2020

Dataset Cleaning -- A Cross Validation Methodology for Large Facial Datasets using Face Recognition

Viktor Varkarakis, Peter Corcoran

In recent years, large "in the wild" face datasets have been released in an attempt to facilitate progress in tasks such as face detection, face recognition, and other tasks. Most…

eess.IV2020

Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages

Muhammad Ali Farooq, Asma Khatoon, Viktor Varkarakis +1

Technology-assisted platforms provide reliable solutions in almost every field these days. One such important application in the medical field is the skin cancer classification in…

cs.CV20191 cited

Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

Viktor Varkarakis, Shabab Bazrafkan, Peter Corcoran

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low comple…