Author:
Abdulrazaq Maiwan B.,Mahmood Mayyadah R.,Zeebaree Subhi R. M.,Abdulwahab Mohammad H.,Zebari Rizgar R.,Sallow Amira B.
Abstract
Abstract
Face expression recognition technology is one of the most recently developed fields in machine learning and has profoundly helped its users through forensic, security, and biometric applications. Many researchers and program developers have allocated their time and energy to figure out various techniques which would add to the technology’s functionality and accuracy. Face expression recognition is a complicated computational process in which is implemented via analyzing changes in facial traits that follow different emotional reactions. This paper endeavors to inspect accuracy ratio of six classifiers based on Relief-F feature selection method, relying on the utilization of the minimum quantity of attributes. The classifiers in which the paper attempts to inspect are Multi-Layer Perceptron, Random Forest, Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Radial Basis Function. The experiment illustrates that K-Nearest Neighbor is the most accurate classifier with the total accuracy ratio of 94.93% amongst the rest when applied on CK+ Dataset.
Subject
General Physics and Astronomy
Reference51 articles.
1. Facial expression recognition: A survey;Huang;Symmetry,2019
2. Facial Expression Recognition based on Hybrid Feature Extraction Techniques with Different Classifiers;Dino;TEST Engineering & Management,2020
3. Facial expression analysis and expression-invariant face recognition by manifold-based synthesis;Peng;Machine Vision and Applications,2018
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