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Depression level detection from facial emotion recognition using image processing

Adolescent depression is increasing daily at an alarming rate. Depression can be considered as a major cause of suicidal ideation and leads to significant impairment in daily life. Depression signs could be identified in peoples’ speech, facial expressions and in the use of language. We consider our...

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Main Authors: N. A., Lili, M. R., Nurul Amiraa, M., MasRina, N., Nurul Amelina
Format: Article
Published: Springer Singapore 2022
Online Access:http://psasir.upm.edu.my/id/eprint/100895/
https://link.springer.com/chapter/10.1007/978-981-16-8515-6_56
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spelling my.upm.eprints.1008952023-07-26T03:15:20Z http://psasir.upm.edu.my/id/eprint/100895/ Depression level detection from facial emotion recognition using image processing N. A., Lili M. R., Nurul Amiraa M., MasRina N., Nurul Amelina Adolescent depression is increasing daily at an alarming rate. Depression can be considered as a major cause of suicidal ideation and leads to significant impairment in daily life. Depression signs could be identified in peoples’ speech, facial expressions and in the use of language. We consider our study can help in the development of new solutions to deal with the early detection or diagnose of depression using facial emotion. Therefore, the objective of this study is to detect the level of depression using facial emotion via mobile application. This application provides user with the facial emotion recognition feature and a set of questions that are used to measure the level of depression of the user. This application will generate the total severity result of the depression alongside with the self-treatment and contact helplines recommendations. The added values of this application are the combination of facial emotion values and the questionnaire score to calculate the level of depression. The application would then recommend the types of treatment best suited for the user. We trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from the user’s face expression. To predict depression, the face of the user will be captured, then using Gabor filters, the facial features are extracted. Classification of these facial features is done using Cascade and PCA classifier. The level of depression is identified by calculating the number of negative emotions present in the image captured. We used the F-measure scores as the performance score on the result gained. Overall result still in moderate level which is 76.7% due to low number of samples and features obtained. Springer Singapore 2022-03-26 Article PeerReviewed N. A., Lili and M. R., Nurul Amiraa and M., MasRina and N., Nurul Amelina (2022) Depression level detection from facial emotion recognition using image processing. Proceedings of the 8th International Conference on Computational Science and Technology, 835. 739 - 750. ISSN 1876-1100; ESSN: 1876-1119 https://link.springer.com/chapter/10.1007/978-981-16-8515-6_56 10.1007/978-981-16-8515-6_56
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Adolescent depression is increasing daily at an alarming rate. Depression can be considered as a major cause of suicidal ideation and leads to significant impairment in daily life. Depression signs could be identified in peoples’ speech, facial expressions and in the use of language. We consider our study can help in the development of new solutions to deal with the early detection or diagnose of depression using facial emotion. Therefore, the objective of this study is to detect the level of depression using facial emotion via mobile application. This application provides user with the facial emotion recognition feature and a set of questions that are used to measure the level of depression of the user. This application will generate the total severity result of the depression alongside with the self-treatment and contact helplines recommendations. The added values of this application are the combination of facial emotion values and the questionnaire score to calculate the level of depression. The application would then recommend the types of treatment best suited for the user. We trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from the user’s face expression. To predict depression, the face of the user will be captured, then using Gabor filters, the facial features are extracted. Classification of these facial features is done using Cascade and PCA classifier. The level of depression is identified by calculating the number of negative emotions present in the image captured. We used the F-measure scores as the performance score on the result gained. Overall result still in moderate level which is 76.7% due to low number of samples and features obtained.
format Article
author N. A., Lili
M. R., Nurul Amiraa
M., MasRina
N., Nurul Amelina
spellingShingle N. A., Lili
M. R., Nurul Amiraa
M., MasRina
N., Nurul Amelina
Depression level detection from facial emotion recognition using image processing
author_facet N. A., Lili
M. R., Nurul Amiraa
M., MasRina
N., Nurul Amelina
author_sort N. A., Lili
title Depression level detection from facial emotion recognition using image processing
title_short Depression level detection from facial emotion recognition using image processing
title_full Depression level detection from facial emotion recognition using image processing
title_fullStr Depression level detection from facial emotion recognition using image processing
title_full_unstemmed Depression level detection from facial emotion recognition using image processing
title_sort depression level detection from facial emotion recognition using image processing
publisher Springer Singapore
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/100895/
https://link.springer.com/chapter/10.1007/978-981-16-8515-6_56
_version_ 1773545504998686720
score 13.211869

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