Ghulam Gilanie^1,*, Sana Cheema¹, Akkasha Latif¹, Anum Saher¹, Muhammad Ahsan¹, Hafeez Ullah², Diya Oommen³

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 57-71, 2024, DOI:10.32604/iasc.2024.041535

Abstract Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient. It affects a large percentage of people globally, who fluctuate between depression and mania, or vice versa. A pleasant or unpleasant mood is more than a reflection of a state of mind. Normally, it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio, so automated procedures are the best options to diagnose and verify the severity of bipolar. In this research work, facial micro-expressions have been used for bipolar detection using… More >

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

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2513-2528, 2022, DOI:10.32604/cmc.2022.028229

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.… More >