Facial Expression Recognition Using a Novel Modeling of Combined Gray Local Binary Pattern

Facial Expression Recognition (FER) is an active research field at present. Deep learning is a good method that is widely used in this field but it has extreme hardware requirements and it is hard to apply in normal terminal devices. So, many other methods are being researched to apply FER in such devices and systems. This work proposes fresh modeling of Combined Gray Local Binary Pattern (CGLBP) for extracting features in facial expression recognition to enhance the recognition rate that can apply FER in the kind of devices and systems. The work included the main steps such as the technique of cropping an input face image from a camera or dataset, the approach of dividing face images into nonoverlap regions for extracting LBP features, applying the fresh modeling of Combined Gray Local Binary Pattern (CGLBP) for extracting features, using uniform feature to reduce the lengths of descriptors, and finally using Support Vector Machine (SVM) for emotion classification. Four popular facial emotion datasets are used in experiments and their results demonstrate that the recognition rate of the proposed method is better in comparison with two types of existent features: Local Binary Pattern (LBP) and Combined Local Binary Pattern (CLBP). The accuracy of experiments performed on four facial expression datasets with different sizes is from about 95% to more than 99%.