Facial Expression Recognition for Traumatic Brain Injured Patients

Publikation: Forskning - peer reviewKonferenceabstrakt i proceeding

Abstrakt

In this paper, we investigate the issues associated with facial expression recognition of Traumatic Brain Insured (TBI) patients in a realistic scenario. These patients have restricted or limited muscle movements with reduced facial expressions along with non-cooperative behavior, impaired reasoning and inappropriate responses. All these factors make automatic understanding of their expressions more complex. While the existing facial expression recognition systems showed high accuracy by taking data from healthy subjects, their performance is yet to be proved for real TBI patient data by considering the aforementioned challenges. To deal with this, we devised scenarios for data collection from the real TBI patients, collected data which is very challenging to process, devised effective way of data preprocessing so that good quality faces can be extracted from the patients facial video for expression analysis, and finally, employed a state-of-the-art deep learning framework to exploit spatio-temporal information of facial video frames in expression analysis. The experimental results confirms the difficulty in processing real TBI patients data, while showing that better face quality ensures better performance in this case.
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Detaljer

In this paper, we investigate the issues associated with facial expression recognition of Traumatic Brain Insured (TBI) patients in a realistic scenario. These patients have restricted or limited muscle movements with reduced facial expressions along with non-cooperative behavior, impaired reasoning and inappropriate responses. All these factors make automatic understanding of their expressions more complex. While the existing facial expression recognition systems showed high accuracy by taking data from healthy subjects, their performance is yet to be proved for real TBI patient data by considering the aforementioned challenges. To deal with this, we devised scenarios for data collection from the real TBI patients, collected data which is very challenging to process, devised effective way of data preprocessing so that good quality faces can be extracted from the patients facial video for expression analysis, and finally, employed a state-of-the-art deep learning framework to exploit spatio-temporal information of facial video frames in expression analysis. The experimental results confirms the difficulty in processing real TBI patients data, while showing that better face quality ensures better performance in this case.
OriginalsprogEngelsk
TitelVISAPP 2018 : 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.
Antal sider9
ForlagSCITEPRESS Digital Library
Publikationsdato22 nov. 2017
Sider1
StatusAccepteret/In press - 22 nov. 2017
PublikationsartForskning
Peer reviewJa
BegivenhedInternational Conference on Computer Vision Theory and Applications - Funchal, Madeira, Portugal, Madeira, Portugal
Varighed: 27 jan. 201829 jan. 2018
http://visapp.visigrapp.org/Home.aspx

Konference

KonferenceInternational Conference on Computer Vision Theory and Applications
LokationFunchal, Madeira, Portugal
LandPortugal
ByMadeira
Periode27/01/201829/01/2018
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