Title: An optimised learning strategy for analysing mentally disordered children's activity based on facial expressions

Authors: Mayuri N. Panpaliya; Pritish A. Tijare

Addresses: Computer Science and Engineering Department, Sipna College of Engineering and Technology, Amravati, Maharashtra, India ' Computer Science and Engineering Department, Sipna College of Engineering and Technology, Amravati, Maharashtra, India

Abstract: Facial expressions play a crucial role in non-verbal communication and are used to recognise present state of mind in humans. A lot of research has been carried out for determining human emotions from facial expressions. However, the contribution is lesser in the case of children having mental illness. This article presents a novel intelligent convolution neural-based buffalo optimisation (CNbBO) model to detect and predict, activities of mentally ill children based on their facial expressions. Initially, machine learning (ML) algorithms are used for pre-processing and feature extraction. Facial emotions are classified with the help of optimisation fitness function from these features. Then, the second fitness function at layer two is updated to track the activities, enhance the detection rate, as well as to improve classification accuracy. Results shows the performance of the developed algorithm is promising with an accuracy of 94%, which is better than present available techniques.

Keywords: mental disorder; mental illness; facial expression; feature extraction; classification accuracy.

DOI: 10.1504/IJBM.2025.149671

International Journal of Biometrics, 2025 Vol.17 No.6, pp.615 - 638

Received: 21 Mar 2025
Accepted: 07 Jun 2025
Published online: 10 Nov 2025 *

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