Open Access

ARTICLE

A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

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

1 Department of Artificial Intelligence, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
2 Biophotonics Imaging Techniques Laboratory, Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
3 West High School, Salt Lake City, UT, 84103, USA

* Corresponding Author: Ghulam Gilanie. Email:

Intelligent Automation & Soft Computing 2024, 39(1), 57-71. https://doi.org/10.32604/iasc.2024.041535

Received 26 April 2023; Accepted 12 January 2024; Issue published 29 March 2024

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 the proposed Convolutional Neural Network (CNN)-based model. Facial Action Coding System (FACS) is used to extract micro-expressions called Action Units (AUs) connected with sad, happy, and angry emotions. Experiments have been conducted on a dataset collected from Bahawal Victoria Hospital, Bahawalpur, Pakistan, Using the Patient Health Questionnaire-15 (PHQ-15) to infer a patient’s mental state. The experimental results showed a validation accuracy of 98.99% for the proposed CNN model while classification through extracted features Using Support Vector Machines (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT) obtained 99.9%, 98.7%, and 98.9% accuracy, respectively. Overall, the outcomes demonstrated the stated method’s superiority over the current best practices.

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Keywords

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