Detecting depression in videos using uniformed local binary pattern on facial features

Bryan G. Dadiz, Conrado R. Ruiz

Research output: Book chapterConference contributionpeer-review

9 Citations (Scopus)

Abstract

The paper presents the classification model of detecting depression based on local binary pattern (LBP) texture features. The study used the video recording from the SEMAINE database. The face image is cropped from a video and extracting the Uniformed LBP features in every single frame. Video keyframe extraction technique was applied to improve frame sampling to a video. Using the SVM with RBF kernel on the original ULBP features, result showed an accuracy of 98% on identifying a depressed person from a video. Also, part of the classification is to implement Principal Component Analysis on the original ULBP features to analyze facial signals by comparing both of the accuracy results. Using the original ULBP features with SVM applying radial basis function kernel, it resulted higher in accuracy whereas the result of using only ten features computed from the PCA of the original ULBP features. The result of the PCA decreased by 5% gaining only 93% in accuracy applying the same cost and gamma values of SVM RBF kernel used on the original ULBP features.

Original languageEnglish
Title of host publicationComputational Science and Technology - 5th ICCST 2018
EditorsRayner Alfred, Ag Asri Ag Ibrahim, Yuto Lim, Patricia Anthony
PublisherSpringer Verlag
Pages413-422
Number of pages10
ISBN (Print)9789811326219
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event5th International Conference on Computational Science and Technology, ICCST 2018 - Kota Kinabalu, Malaysia
Duration: 29 Aug 201830 Aug 2018

Publication series

NameLecture Notes in Electrical Engineering
Volume481
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference5th International Conference on Computational Science and Technology, ICCST 2018
Country/TerritoryMalaysia
CityKota Kinabalu
Period29/08/1830/08/18

Keywords

  • Computer vision
  • Depression analysis
  • Facial features
  • Local binary pattern

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