An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition

Abstract

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.

A Novel Feature Extraction Descriptor for Face Recognition

“…LBP describes smaller details of appearance, while Gabor processes the structure of faces over a wider range of scales [15]. LBP is recognized as a powerful feature extraction method for face recognition and texture classification [16]. LBPH uses a histogram by dividing the cropped face image blocks and counting the histogram for each block by integrating the histogram blocks to introduce the final feature vector.…”