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

Bryan G. Dadiz, Conrado R. Ruiz

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

9 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaComputational Science and Technology - 5th ICCST 2018
EditoresRayner Alfred, Ag Asri Ag Ibrahim, Yuto Lim, Patricia Anthony
EditorialSpringer Verlag
Páginas413-422
Número de páginas10
ISBN (versión impresa)9789811326219
DOI
EstadoPublicada - 2019
Publicado de forma externa
Evento5th International Conference on Computational Science and Technology, ICCST 2018 - Kota Kinabalu, Malasia
Duración: 29 ago 201830 ago 2018

Serie de la publicación

NombreLecture Notes in Electrical Engineering
Volumen481
ISSN (versión impresa)1876-1100
ISSN (versión digital)1876-1119

Conferencia

Conferencia5th International Conference on Computational Science and Technology, ICCST 2018
País/TerritorioMalasia
CiudadKota Kinabalu
Período29/08/1830/08/18

Huella

Profundice en los temas de investigación de 'Detecting depression in videos using uniformed local binary pattern on facial features'. En conjunto forman una huella única.

Citar esto