Resumé
| Originalsprog | Engelsk |
|---|---|
| Titel | VISAPP 2018 : 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. |
| Redaktører | Alain Tremeau, Jose Braz, Francisco Imai |
| Antal sider | 9 |
| Vol/bind | 4 |
| Forlag | SCITEPRESS Digital Library |
| Publikationsdato | 2018 |
| Sider | 522-530 |
| ISBN (Trykt) | 978-989-758-290-5 |
| ISBN (Elektronisk) | 978-989-758-290-5 |
| DOI | |
| Status | Udgivet - 2018 |
| Begivenhed | International Conference on Computer Vision Theory and Applications - Funchal, Madeira, Portugal, Madeira, Portugal Varighed: 27 jan. 2018 → 29 jan. 2018 http://visapp.visigrapp.org/Home.aspx |
Konference
| Konference | International Conference on Computer Vision Theory and Applications |
|---|---|
| Lokation | Funchal, Madeira, Portugal |
| Land | Portugal |
| By | Madeira |
| Periode | 27/01/2018 → 29/01/2018 |
| Internetadresse |
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Facial Expression Recognition for Traumatic Brain Injured Patients. / Ilyas, Chaudhary Muhammad Aqdus; Haque, Mohammad Ahsanul; Rehm, Matthias; Nasrollahi, Kamal; Moeslund, Thomas B.
VISAPP 2018: 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.. red. / Alain Tremeau; Jose Braz; Francisco Imai. Bind 4 SCITEPRESS Digital Library, 2018. s. 522-530.Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review
TY - GEN
T1 - Facial Expression Recognition for Traumatic Brain Injured Patients
AU - Ilyas, Chaudhary Muhammad Aqdus
AU - Haque, Mohammad Ahsanul
AU - Rehm, Matthias
AU - Nasrollahi, Kamal
AU - Moeslund, Thomas B.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Computer Vision
KW - Face Detection
KW - Facial Landmarks
KW - Facial Expressions
KW - Convolution Neural Networks
KW - Long-Short Term Memory
KW - Traumatic Brain Injured Patients
UR - http://www.scopus.com/inward/record.url?scp=85047860286&partnerID=8YFLogxK
U2 - 10.5220/0006721305220530
DO - 10.5220/0006721305220530
M3 - Article in proceeding
SN - 978-989-758-290-5
VL - 4
SP - 522
EP - 530
BT - VISAPP 2018
A2 - Tremeau, Alain
A2 - Braz, Jose
A2 - Imai, Francisco
PB - SCITEPRESS Digital Library
ER -