@Article{cmc.2022.028229,
AUTHOR = {Ghulam Gilanie, Mahmood ul Hassan, Mutyyba Asghar, Ali Mustafa Qamar, Hafeez Ullah, Rehan Ullah Khan, Nida Aslam, Irfan Ullah Khan},
TITLE = {An Automated and Real-time Approach of Depression Detection from Facial Micro-expressions},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {73},
YEAR = {2022},
NUMBER = {2},
PAGES = {2513--2528},
URL = {http://www.techscience.com/cmc/v73n2/48348},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2022.028229}
}