activity
20192025
most citedFacial Expression Recognition Using Disentangled Adversarial Learning

8 citations · 34 across the 10 of their papers we have counts for

collaborators
Showing 2019Show all

6 papers · 1 filter

cs.CV2019★ 1 cited

Facial Expression Representation Learning by Synthesizing Expression Images

Kamran Ali, Charles E. Hughes

Representations used for Facial Expression Recognition (FER) usually contain expression information along with identity features. In this paper, we propose a novel Disentangled Exp…

cs.CV2019★ 7 cited

All-In-One: Facial Expression Transfer, Editing and Recognition Using A Single Network

Kamran Ali, Charles E. Hughes

In this paper, we present a unified architecture known as Transfer-Editing and Recognition Generative Adversarial Network (TER-GAN) which can be used: 1. to transfer facial express…

cs.CV2019★ 4 cited

Facial Expression Recognition Using Human to Animated-Character Expression Translation

Kamran Ali, Ilkin Isler, Charles Hughes

Facial expression recognition is a challenging task due to two major problems: the presence of inter-subject variations in facial expression recognition dataset and impure expressi…

cs.CV2019★ 8 cited

Facial Expression Recognition Using Disentangled Adversarial Learning

Kamran Ali, Charles E. Hughes

The representation used for Facial Expression Recognition (FER) usually contain expression information along with other variations such as identity and illumination. In this paper,…

cs.CV2019★ 4 cited

Brain MRI Segmentation using Rule-Based Hybrid Approach

Mustansar Fiaz, Kamran Ali, Abdul Rehman +2

Medical image segmentation being a substantial component of image processing plays a significant role to analyze gross anatomy, to locate an infirmity and to plan the surgical proc…

cs.SE2019

A Reliabel and an efficient web testing system

Kamran Ali, Xia Xiaoling

To improve the reliability and efficiency of Web Software, the Testing Team should be creative and innovative. The experience and intuition of Tester also matters a lot and most of…