activity
20172020
most citedLimitations and Biases in Facial Landmark Detection -- An Empirical Study on Older Adults with Dementia

8 citations · 8 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2020

Estimation of Orofacial Kinematics in Parkinson's Disease: Comparison of 2D and 3D Markerless Systems for Motion Tracking

Diego L. Guarin, Aidan Dempster, Andrea Bandini +2

Orofacial deficits are common in people with Parkinson's disease (PD) and their evolution might represent an important biomarker of disease progression. We are developing an automa…

cs.CV2019

Toward an Automatic System for Computer-Aided Assessment in Facial Palsy

Diego L. Guarin, Yana Yunusova, Babak Taati +7

Importance: Machine learning (ML) approaches to facial landmark localization carry great clinical potential for quantitative assessment of facial function as they enable high-throu…

cs.CV20198 cited

Limitations and Biases in Facial Landmark Detection -- An Empirical Study on Older Adults with Dementia

Azin Asgarian, Shun Zhao, Ahmed B. Ashraf +5

Accurate facial expression analysis is an essential step in various clinical applications that involve physical and mental health assessments of older adults (e.g. diagnosis of pai…

cs.CV2018

Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies

Ahmed Ashraf, Shehroz Khan, Nikhil Bhagwat +2

Medical imaging machine learning algorithms are usually evaluated on a single dataset. Although training and testing are performed on different subsets of the dataset, models built…

cs.LG2018

A Hybrid Instance-based Transfer Learning Method

Azin Asgarian, Parinaz Sobhani, Ji Chao Zhang +4

In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models…

cs.CV2017

Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning

Azin Asgarian, Ahmed Bilal Ashraf, David Fleet +1

Active appearance models (AAMs) are a class of generative models that have seen tremendous success in face analysis. However, model learning depends on the availability of detailed…