Detecting depression in videos using uniformed local binary pattern on facial features
College of Computer Studies
Lecture Notes in Electrical Engineering
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. © Springer Nature Singapore Pte Ltd. 2019.
Digitial Object Identifier (DOI)
Dadiz, B. G., & Ruiz, C. R. (2019). Detecting depression in videos using uniformed local binary pattern on facial features. Lecture Notes in Electrical Engineering, 481, 413-422. https://doi.org/10.1007/978-981-13-2622-6_40
Facial expression; Optical pattern recognition; Depression, Mental—Diagnosis—Automation; Computer vision