A DETAILED ANALYSIS OF EMOTION RECOGNITION USING HUMAN FACIAL FEATURES IN INTELLIGENT COMPUTING SYSTEMS - Noshaba Khan; Umair Paracha; Azeem Akram; Jawaid Iqbal (Spectrum of Engineering Sciences, 2025)
Emotion recognition through human facial features has emerged as a vital area of research in the field of intelligent computing systems, with broad applications in human-computer interaction, surveillance, healthcare, and user experience enhancement. This paper presents a comprehensive analysis of facial expression-based emotion recognition, focusing on its theoretical foundations, practical implementation, and integration into intelligent systems. The study explores the psychological models of emotions, particularly Ekman’s six basic emotions, and their physiological manifestations on the human face. It further investigates various computational techniques used to detect and classify emotions, including traditional machine learning algorithms such as Support Vector Machines (SVM), as well as advanced deep learning models like Convolutional Neural Networks (CNNs). Multiple publicly available datasets, such as FER-2013 and CK+, are examined to evaluate system performance and accuracy. The paper outlines a step-by-step pipeline for emotion recognition, encompassing face detection, feature extraction, classification, and post-processing. Special emphasis is placed on the role of data preprocessing, real-time performance, and generalization across diverse populations. Experimental results highlight the effectiveness and limitations of current techniques, with quantitative metrics provided to support the analysis. The study also discusses challenges such as variability in lighting, occlusions, subjectivity of emotional expression, and cultural differences. Finally, it outlines future directions, including the integration of multimodal data (e.g., voice, gestures), ethical concerns, and the potential for real-time deployment in adaptive intelligent systems. This detailed investigation contributes to a deeper understanding of how emotion recognition can be effectively modeled and utilized within the framework of intelligent computing.