| Autor: |
Sidhom, Ones, Ghazouani, Haythem, Barhoumi, Walid, Chehri, Abdellah |
| Předmět: |
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| Zdroj: |
Procedia Computer Science; 2024, Vol. 246, p3044-3053, 10p |
| Abstrakt: |
Facial Expression Recognition (FER) is a crucial aspect in various domains, given its significance in understanding human emotions. However, designing efficient FER systems entails addressing challenges in feature extraction and selection. While previous studies have primarily focused on static feature selection methods, these approaches often struggle with spontaneous expressions due to the unique facial characteristics of each individual. To address this challenge, we implemented a Facial Morphology-Guided Feature Selection Method that combines texture features using Local Binary Pattern histograms and geometric features employing linear and eccentricity features. Subsequently, we employ Recursive Feature Elimination (RFE) and Binarized Genetic Algorithm (BGA) algorithms for feature selection, combining their outputs to identify the optimal subset of features tailored for each face. Experimental validation using the CK+ and DISFA datasets demonstrates the effectiveness of our approach in enhancing facial expression recognition accuracy. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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