Comparative Analysis of Facial Expression Recognition Using Image-Based and Landmark-Based Methods

This study compares the effectiveness of image-based and landmark-based methods for facial expression recognition (FER) in classifying hurt and normal facial expressions, utilizing datasets from the Delaware Pain Database and UTKFace. Five machine learning models are assessed, including convolutional neural networks (CNN), support vector machines (SVM), random forest classifier (RFC), logistic regression classifier (LRC), and gradient boosting classifier (GBC). The findings indicate that CNN achieves the highest accuracy at 95% when using landmark-based features, while SVM and GBC also perform admirably with these features. Conversely, LRC exhibits inconsistent results, especially when relying on image-based features. These findings offer valuable insights into the strengths and weaknesses of each approach, guiding the selection of effective FER techniques.