Resumé

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.
RedaktørerAlain Tremeau, Jose Braz, Francisco Imai
Antal sider9
Vol/bind4
ForlagSCITEPRESS Digital Library
Publikationsdato2018
Sider522-530
ISBN (Trykt)978-989-758-290-5
ISBN (Elektronisk)978-989-758-290-5
DOI
StatusUdgivet - 2018
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
Internetadresse

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Brain
Muscle
Processing

Emneord

    Citer dette

    Ilyas, C. M. A., Haque, M. A., Rehm, M., Nasrollahi, K., & Moeslund, T. B. (2018). Facial Expression Recognition for Traumatic Brain Injured Patients. I A. Tremeau, J. Braz, & F. Imai (red.), VISAPP 2018: 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. (Bind 4, s. 522-530). SCITEPRESS Digital Library. https://doi.org/10.5220/0006721305220530
    Ilyas, Chaudhary Muhammad Aqdus ; Haque, Mohammad Ahsanul ; Rehm, Matthias ; Nasrollahi, Kamal ; Moeslund, Thomas B. / Facial Expression Recognition for Traumatic Brain Injured Patients. 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
    @inproceedings{e886fd3bf91a44bead75b1a4df82a58c,
    title = "Facial Expression Recognition for Traumatic Brain Injured Patients",
    abstract = "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.",
    keywords = "Computer Vision, Face Detection, Facial Landmarks, Facial Expressions, Convolution Neural Networks, Long-Short Term Memory, Traumatic Brain Injured Patients",
    author = "Ilyas, {Chaudhary Muhammad Aqdus} and Haque, {Mohammad Ahsanul} and Matthias Rehm and Kamal Nasrollahi and Moeslund, {Thomas B.}",
    year = "2018",
    doi = "10.5220/0006721305220530",
    language = "English",
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    Ilyas, CMA, Haque, MA, Rehm, M, Nasrollahi, K & Moeslund, TB 2018, Facial Expression Recognition for Traumatic Brain Injured Patients. i A Tremeau, J Braz & F Imai (red), VISAPP 2018: 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.. bind 4, SCITEPRESS Digital Library, s. 522-530, Madeira, Portugal, 27/01/2018. https://doi.org/10.5220/0006721305220530

    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 proceedingKonferenceartikel i proceedingForskningpeer review

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    AU - Ilyas, Chaudhary Muhammad Aqdus

    AU - Haque, Mohammad Ahsanul

    AU - Rehm, Matthias

    AU - Nasrollahi, Kamal

    AU - Moeslund, Thomas B.

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    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.

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    KW - Face Detection

    KW - Facial Landmarks

    KW - Facial Expressions

    KW - Convolution Neural Networks

    KW - Long-Short Term Memory

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    A2 - Imai, Francisco

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    Ilyas CMA, Haque MA, Rehm M, Nasrollahi K, Moeslund TB. Facial Expression Recognition for Traumatic Brain Injured Patients. I Tremeau A, Braz J, Imai F, red., VISAPP 2018: 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.. Bind 4. SCITEPRESS Digital Library. 2018. s. 522-530 https://doi.org/10.5220/0006721305220530