Paper Info
Title
Learning to Detect Genuine versus Posed Pain from Facial Expressions using Residual Generative Adversarial Networks
Abstract
We present a novel approach based on Residual Generative Adversarial Network (R-GAN) to discriminate genuine pain expression from posed pain expression by magnifying the subtle changes in the face. In addition to the adversarial task, the discriminator network in R-GAN estimates the intensity level of the pain. Moreover, we propose a novel Weighted Spatiotemporal Pooling (WSP) to capture and encode the appearance and dynamic of a given video sequence into an image map. In this way, we are able to transform any video into an image map embedding subtle variations in the facial appearance and dynamics. This allows using any pre-trained model on still images for video analysis. Our extensive experiments show that our proposed framework achieves promising results compared to state-of-the-art approaches on three benchmark databases, i.e., UNBC-McMaster Shoulder Pain, BioVid Head Pain, and STOIC.
Year
DOI
Venue
2019
10.1109/FG.2019.8756540
2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)
Keywords
Field
DocType
UNBC-McMaster Shoulder Pain,BioVid Head Pain,facial expressions,residual generative adversarial networks,R-GAN,genuine pain expression,posed pain expression,adversarial task,discriminator network,image map,facial appearance,facial dynamics,weighted spatiotemporal pooling,pain intensity level estimation,learning,face subtle changes,video sequence
Image map,ENCODE,Residual,Embedding,Discriminator,Pattern recognition,Computer science,Pooling,Facial expression,Artificial intelligence,Generative grammar
Conference
ISSN
ISBN
Citations 
2326-5396
978-1-7281-0090-6
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Mohammad Tavakolian120.70
Carlos Guillermo Bermudez Cruces210.35
Abdenour Hadid33305146.00