activity
20182020
most citedTowards Building A Facial Identification System Using Quantum Machine Learning Techniques

3 citations · 3 across the 2 of their papers we have counts for

collaborators

6 papers

quant-ph20203 cited

Towards Building A Facial Identification System Using Quantum Machine Learning Techniques

Philip Easom-McCaldin, Ahmed Bouridane, Ammar Belatreche +1

In the modern world, facial identification is an extremely important task in which many applications rely on high performing algorithms to detect faces efficiently. Whilst classica…

cs.CV2019

Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila

Khan Faraz, Ahmed Bouridane, Richard Jiang +3

Gene expression of social actions in Drosophilae has been attracting wide interest from biologists, medical scientists and psychologists. Gene-edited Drosophilae have been used as…

cs.LG2019

Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis

Gary Storey, Ahmed Bouridane, Richard Jiang +1

While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investig…

cs.CV2019

3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks

Gary Storey, Richard Jiang, Shelagh Keogh +2

The capability to perform facial analysis from video sequences has significant potential to positively impact in many areas of life. One such area relates to the medical domain to…

cs.CV2019

Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks

Ranjith Dinakaran, Philip Easom, Li Zhang +3

In this work, we examine the feasibility of applying Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) as data-processing technique to han…

cs.CV2018

Deep Learning based Pedestrian Detection at Distance in Smart Cities

Ranjith K Dinakaran, Philip Easom, Ahmed Bouridane +4

Generative adversarial networks (GANs) have been promising for many computer vision problems due to their powerful capabilities to enhance the data for training and test. In this p…