Facial Landmark Activity Features for Depression Screening

Brian Sumali; Yasue Mitsukura; Yuuki Tazawa; Taishiro Kishimoto

doi:10.23919/SICE.2019.8859798

Facial Landmark Activity Features for Depression Screening

Brian Sumali, Yasue Mitsukura, Yuuki Tazawa, Taishiro Kishimoto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Depression is the most common mood disorder in the world which is also the leading cause of suicide. Depression might be diagnosed using varying modalities, as it affects many aspects of the patient, not only the patient will appear melancholic, but the patient's sleep quality and life quality might also be disrupted. In this study, we aim to extract facial landmark features unique to depression patients and build a machine learning model to classify the depression severity. The facial landmark activities considered in this study include: speed statistics of the landmarks, the standard deviation of eye pupils, and statistical features of mouth area. We found several facial landmark activity features with significant differences between healthy volunteers and depressed patients. We also successfully built machine learning models for automatic depression severity prediction.

Original language	English
Title of host publication	2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1376-1381
Number of pages	6
ISBN (Electronic)	9784907764678
DOIs	https://doi.org/10.23919/SICE.2019.8859798
Publication status	Published - 2019 Sep
Event	58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019 - Hiroshima, Japan Duration: 2019 Sep 10 → 2019 Sep 13

Publication series

Name	2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019

Conference

Conference	58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019
Country	Japan
City	Hiroshima
Period	19/9/10 → 19/9/13

Keywords

Automatic diagnosis
Depression
Facial features
Machine learning

ASJC Scopus subject areas

Artificial Intelligence
Industrial and Manufacturing Engineering
Safety, Risk, Reliability and Quality
Control and Optimization
Instrumentation

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

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10.23919/SICE.2019.8859798

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Sumali, B., Mitsukura, Y., Tazawa, Y., & Kishimoto, T. (2019). Facial Landmark Activity Features for Depression Screening. In 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019 (pp. 1376-1381). [8859798] (2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/SICE.2019.8859798

Facial Landmark Activity Features for Depression Screening. / Sumali, Brian; Mitsukura, Yasue; Tazawa, Yuuki; Kishimoto, Taishiro.

2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1376-1381 8859798 (2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sumali, B, Mitsukura, Y, Tazawa, Y & Kishimoto, T 2019, Facial Landmark Activity Features for Depression Screening. in 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019., 8859798, 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1376-1381, 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019, Hiroshima, Japan, 19/9/10. https://doi.org/10.23919/SICE.2019.8859798

Sumali B, Mitsukura Y, Tazawa Y, Kishimoto T. Facial Landmark Activity Features for Depression Screening. In 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1376-1381. 8859798. (2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019). https://doi.org/10.23919/SICE.2019.8859798

Sumali, Brian ; Mitsukura, Yasue ; Tazawa, Yuuki ; Kishimoto, Taishiro. / Facial Landmark Activity Features for Depression Screening. 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1376-1381 (2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019).

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abstract = "Depression is the most common mood disorder in the world which is also the leading cause of suicide. Depression might be diagnosed using varying modalities, as it affects many aspects of the patient, not only the patient will appear melancholic, but the patient's sleep quality and life quality might also be disrupted. In this study, we aim to extract facial landmark features unique to depression patients and build a machine learning model to classify the depression severity. The facial landmark activities considered in this study include: speed statistics of the landmarks, the standard deviation of eye pupils, and statistical features of mouth area. We found several facial landmark activity features with significant differences between healthy volunteers and depressed patients. We also successfully built machine learning models for automatic depression severity prediction.",

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AB - Depression is the most common mood disorder in the world which is also the leading cause of suicide. Depression might be diagnosed using varying modalities, as it affects many aspects of the patient, not only the patient will appear melancholic, but the patient's sleep quality and life quality might also be disrupted. In this study, we aim to extract facial landmark features unique to depression patients and build a machine learning model to classify the depression severity. The facial landmark activities considered in this study include: speed statistics of the landmarks, the standard deviation of eye pupils, and statistical features of mouth area. We found several facial landmark activity features with significant differences between healthy volunteers and depressed patients. We also successfully built machine learning models for automatic depression severity prediction.

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Facial Landmark Activity Features for Depression Screening

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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