000			02174nam a22002417a 4500
003			FT8901
005			20251218091314.0
008			251218b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aQA268.5 B45 2025
082			_a.
100	1		_aBelmonte, Kricel M.; Quilop, Kyla Johnine D.
245			_aEnhancing local binary pattern – support vector machine (LBP-SVM) for improved facial emotion classification
264		1	_a. _b. _cc2025
300			_bUndergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2 text _a text _b text
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: Supervised Machine Learning algorithm like Support Vector Machines (SVMs), has emerged as powerful tools for classification, regression, and anomaly detection tasks. However, despite their high potential for facial emotion classification, SVMs face several challenges, including manual hyperparameter tuning, overfitting in handling overlapping classes and noisy features, and failure to effectively detect action units. Addressing these challenges is important in improving the accuracy and robustness of Facial Emotion Recognition (FER) systems. This study proposed improvements to the Local Binary Pattern – Support Vector Machine (LBP-SVM) framework by incorporating several key techniques. The Grid Search will be used to automatically select the optimal value for C and gamma parameters. For overfitting and noisy features, Recursive Feature Elimination will be used to select the features that contribute the most to class separability. Finally, to further enhance the accuracy of the model, the researchers also combined the features extracted from Facial Action Units to capture the dynamic characteristics of facial expressions. Experimental results on the CK+ datasets show that the enhanced LBP-SVM achieves a highest mean accuracy of 98.34% across 10-fold cross-validation, demonstrating improved robustness in recognizing subtle and complex emotional expressions.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37370 _d37370