id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
aiti-14589	Thanawat Srikaewsiew; Sarunya Kanjanawattana	Comparative Analysis of Facial Expression Recognition Using Image-Based and Landmark-Based Methods	2025	12	.pdf	application/pdf	6045	342	44	This finding underscores the importance of landmark features in enhancing the SVM’s ability to identify complex patterns in facial expression data, corroborating the conclusions of both Sharma et al. (3) Traditional machine learning models (SVM, RFC, GBC) showed strong performance with landmark features, achieving accuracies between 85.5% and 89.5%, making them good alternatives when computing resources are limited.	cache/aiti-14589.pdf	txt/aiti-14589.txt