Abstract

Facial Expression Recognition (FER) is a vital aspect of human-computer interaction with applications in healthcare, education security, and affective computing. Even with the success of deep learning, generalizability, interpretability, and efficiency of most systems, especially in uncontrolled settings, are still problematic. In this study, we propose an enhanced feature extraction technique based on Histograms of Oriented Gradient (HOG) where the central difference operator, not the conventional forward difference, used for gradient estimation. The modification enhances the accuracy of gradients, reduces truncation error, and leads to more stable facial feature descriptors. The enhanced HOG is tested on five popular datasets, CK+, JAFFE, MMI, ExpW, and AffectNet, using three traditional Machine Learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). Experimental results indicate uniform accuracy enhancements across all the classifiers and datasets, with improvements spiking to 7%–10% and recall and F1-score also witnessing marked increases. In this study, RF registered the maximum accuracy, 97.94%, on CK+ and 95.48% on AffectNet, hence solidifying its stability and dependability. This study shows how well mathematical optimization works with classical ML for FER. The approach we suggest provides an easy-to-understand, small, and quick alternative to deep models, making it perfect for real-time and resource-limited applications.