Enhancing Local Binary Pattern-Support Vector Machine (LBP-SVM) for Improved Facial Emotion Classification

Kricel M. Belmonte; Kyla Johnine D. Quilop

Enhancing Local Binary Pattern-Support Vector Machine (LBP-SVM) for Improved Facial Emotion Classification

Author(s)	Kricel M. Belmonte, Kyla Johnine D. Quilop
Country	Philippines
Abstract	Supervised Machine Learning algorithms like Support Vector Machines (SVMs), have emerged as powerful tools for classification, regression, and anomaly detection tasks. However, despite their high potential for emotion classification, SVMs face several challenges, including manual hyperparameter tuning, overfitting, difficulties 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 systems. This study proposed improvements to the Local Binary Pattern - Support Vector Machine (LBP-SVM) framework to address these challenges. The Grid Search will be used to automatically select the optimal value for C and gamma parameters. To address the problem of overfitting and noisy features, Recursive Feature Elimination will be used to select the features that contribute the most to the class separability. Finally, to further enhance the accuracy of the model the researcher also combined the feature extracted from Facial Action Units to capture the dynamic characteristics of facial expressions. Experimental results on the CK+ dataset show the enhanced LBP-SVM achieves 98% accuracy, demonstrating improved robustness for recognizing subtle and complex emotional expressions.
Keywords	Support Vector Machines (SVMs), Facial Emotion Recognition, Local Binary Pattern (LBP), Hyperparameter Tuning, Recursive Feature Elimination
Field	Computer Applications
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-21
Cite This	Enhancing Local Binary Pattern-Support Vector Machine (LBP-SVM) for Improved Facial Emotion Classification - Kricel M. Belmonte, Kyla Johnine D. Quilop - IJFMR Volume 6, Issue 6, November-December 2024.

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6 Cover Page Vol 6 Isu 5 Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Enhancing Local Binary Pattern-Support Vector Machine (LBP-SVM) for Improved Facial Emotion Classification

Share this