Open Access

ARTICLE

An Automated and Real-time Approach of Depression Detection from Facial Micro-expressions

Ghulam Gilanie¹, Mahmood ul Hassan², Mutyyba Asghar¹, Ali Mustafa Qamar^3,*, Hafeez Ullah⁴, Rehan Ullah Khan⁵, Nida Aslam⁶, Irfan Ullah Khan⁶

1 Department of Artificial Intelligence, Faculty of Computing, The Islamia University of Bahawalpur, Pakistan
2 Department of Computer Skills, Deanship of Preparatory Year, Najran University, Najran, Saudi Arabia
3 Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia
4 Biophotonics Imaging Techniques Laboratory, Institute of Physics, The Islamia University of Bahawalpur, Pakistan
5 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
6 Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

* Corresponding Author: Ali Mustafa Qamar. Email:

Computers, Materials & Continua 2022, 73(2), 2513-2528. https://doi.org/10.32604/cmc.2022.028229

Received 05 February 2022; Accepted 10 March 2022; Issue published 16 June 2022

Abstract

Depression is a mental psychological disorder that may cause a physical disorder or lead to death. It is highly impactful on the social-economical life of a person; therefore, its effective and timely detection is needful. Despite speech and gait, facial expressions have valuable clues to depression. This study proposes a depression detection system based on facial expression analysis. Facial features have been used for depression detection using Support Vector Machine (SVM) and Convolutional Neural Network (CNN). We extracted micro-expressions using Facial Action Coding System (FACS) as Action Units (AUs) correlated with the sad, disgust, and contempt features for depression detection. A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in real-time. Experiments have been performed on the dataset obtained from Bahawal Victoria Hospital, Bahawalpur, Pakistan, as per the patient health questionnaire depression scale (PHQ-8); for inferring the mental condition of a patient. The experiments revealed 99.9% validation accuracy on the proposed CNN model, while extracted features obtained 100% accuracy on SVM. Moreover, the results proved the superiority of the reported approach over state-of-the-art methods.

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Keywords

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