Expert System for Detecting Human Emotion Based on Facial Appearance

Student: MUBARAK MUHAMMAD MUSA (Project, 2025)
Department of Computer Science
Federal Polytechnic, Mubi, Adamawa State

Abstract

Human emotion detection plays a crucial role in enhancing the interaction between humans and machines. This paper presents the development of an expert system designed to detect human emotions based on facial appearance. The system leverages image processing techniques and artificial intelligence (AI) models to analyze facial expressions and accurately classify emotions such as happiness, sadness, anger, surprise, fear, and disgust. By extracting key facial features including eye shape, mouth curvature, and eyebrow position and processing them through trained classifiers such as convolutional neural networks (CNNs), the system mimics human expert reasoning in interpreting emotional states. The expert system architecture incorporates a knowledge base, an inference engine, and a user interface, enabling real-time emotion recognition with high accuracy. This innovation holds significant potential in fields such as mental health assessment, customer service, adaptive learning environments, and intelligent surveillance. Future improvements will focus on multi-modal emotion detection that integrates voice and gesture analysis for enhanced precision.

Keywords
Expert system human emotion detection facial recognition emotion analysis AI image processing machine learning facial expression artificial intelligence computer vision
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