Performance analysis of Face Expression Recognition using Feature level Fusion

Face Expression Recognition (FER) is gaining more attention in social communication. It is noted that, face expressions help humans to communicate strongly with emotions. The FER models include preprocessing, feature extraction and classification stages. In this paper, 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 forms 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. The publicly available JAFFE database is used to evaluate the performance of the face expression recognition models. The accuracy of 98.26% and 95.21% is recorded for SVM and KNN classifier.