Open AccessAn Effective Combination of Textures and Wavelet Features for Facial Expression Recognition
Author(s) -
Syed Muhammad Hassan,
Abdullah Alghamdi,
Azeem Hafeez,
Mohammed Hamdi,
Imtiaz Hussain,
Mesfer Alrizq
Publication year - 2021
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4080
Subject(s) - artificial intelligence , pattern recognition (psychology) , local binary patterns , computer science , histogram , feature extraction , facial expression , random forest , perceptron , classifier (uml) , histogram of oriented gradients , wavelet , artificial neural network , image (mathematics)
In order to explore the accompanying examination goals for facial expression recognition, a proper combination of classification and adequate feature extraction is necessary. If inadequate features are used, even the best classifier could fail to achieve accurate recognition. In this paper, a new fusion technique for human facial expression recognition is used to accurately recognize human facial expressions. A combination of Discrete Wavelet Features (DWT), Local Binary Pattern (LBP), and Histogram of Gradients (HoG) feature extraction techniques was used to investigate six human emotions. K-Nearest Neighbors (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Random Forest (RF) were chosen for classification. These algorithms were implemented and tested on the Static Facial Expression in Wild (SWEW) dataset which consists of facial expressions of high accuracy. The proposed algorithm exhibited 87% accuracy which is higher than the accuracy of the individual algorithms.