An Improved Method for Facial Expression Recognition Based on Extended Local Binary Pattern and Differential Features
Sanjana Farial Oishee, Redoan Ur Rahman, Md. Sabbir Hassan, Ahmed Shahriar Adnan, Md Wasi Ul Kabir · 2019
In our study, we made an effort to combine the Extended Local Binary Pattern(ELBP) and Principal Component Analysis (PCA) to weigh out the robust facial features of a person from an image. We set our goal as to develop and perform a low-computational approach on two most widely used databases - JAFFE and Cohn-Kanade based on the differences in the feature vectors to classify the human expressions shown in the images into seven basic emotions-anger, disgust, fear, happiness, sadness, surprise, and neutral. Moreover, we have also proposed a completely unique idea of feature vector generation based on differential method. Our approach yielded test accuracies of 88.29% and 81.04% on the mentioned databases respectively. Furthermore, we have also trained and tested our model on a custom database by mixing up both of these databases and it held an accuracy of 86.89%.