Facial Landmark Activity Features for Depression Screening

Brian Sumali, Yasue Mitsukura, Yuuki Tazawa, Taishiro Kishimoto

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1376-1381
Number of pages6
ISBN (Electronic)9784907764678
DOIs
Publication statusPublished - 2019 Sep
Event58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019 - Hiroshima, Japan
Duration: 2019 Sep 102019 Sep 13

Publication series

Name2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019

Conference

Conference58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019
CountryJapan
CityHiroshima
Period19/9/1019/9/13

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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

Cite this

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