Fuzzy Local Binary Pattern and Weber Local Descriptor for Facial Emotion Classification

Facial emotion is one of the nonverbal interactions in humans that occurs due to facial muscle changes caused by emotional state. For a decade, researchers have conducted research aimed at identifying emotional states. In the education field, students' emotional conditions and their motivation can influence the learning process both directly and indirectly. This paper proposes a facial expression classifier system using characteristic features of Fuzzy Local Binary Pattern (FLBP) and Weber Local Descriptor (WLD). Face detection is carried out in the preprocessing stage using the Viola-Jones algorithm, which cuts the detected faces and resizes them. The characteristic features used in the system are a combination of FLBP and WLD. Then, the classification method uses the Support Vector Machine (SVM). This study aims to facilitate the classification of types of facial expressions, where there are seven facial expressions: disgust, angry, neutral, sad, happy, fear, and surprise. The total data are 203 images, with 133 train data and 70 test data. The combined features of FLBP and WLD provide accuracy, precision, and recall of 92.86% and computation time of 6.19 seconds, respectively. The analysis of multiclass SVM parameters and the performance of each facial expression is also discussed in this paper. Multiclass One-Against-All (OAA) outperforms One-Against-One (OAO).