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02174nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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FT8901 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251218091314.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251218b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA268.5 B45 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Belmonte, Kricel M.; Quilop, Kyla Johnine D. |
| 245 ## - TITLE STATEMENT |
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| Title |
Enhancing local binary pattern – support vector machine (LBP-SVM) for improved facial emotion classification |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Undergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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text |
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text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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unmediated |
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unmediated |
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unmediated |
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volume |
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volume |
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volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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ABSTRACT: 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 ## - STUDY PROGRAM INFORMATION NOTE |
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Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Item type |
Thesis/Dissertation |