Performance Evaluation of Fusion Based Efficient Algorithm for Facial Expression Recognition

Abstract

Objectives: To develop face expression recognition system using JAFFE database and to evaluate the performance of the face expression recognition models. Methods: This study used the FER model based on modified-HoG (Histogram of oriented gradient), LBP (Local Binary Patterns) and Fast Key point detector and BRIEF descriptor (FKBD) to extract the significant features of JAFFE dataset. The features extracted using HoG, LBP and FKBD techniques form a feature vector. Then, the fusion of all the features is carried out at the feature level. The multiclass SVM and KNN classifiers are used to recognize the facial expressions, effectively. Findings: In this work, an effort is made to develop a robust FER model using JAFFE database. It is recorded that, based on the experimental results, the proposed model suits better with a performance rate of 98.26% for SVM and 96.51% for KNN, when compared with the different state-of-the-art methods. Novelty: Many FER models have been developed and adopted for enhancing their quality and to extract the facial features using transform and frequency domains. It is observed that, maximum approaches are based on generating the texture features. The fusion at the feature level using modified HoG, LBP and FKBD is performed and the SVM model is more compatible when compared with other classifiers and it supports one-to-one and one-to-many comparisons’ technique. Keywords: Face Expression Recognition; Local Binary Pattern; Emotions; Nearest Neighbor; Histogram of Gradients