Development of a robust multi-scale featured local binary pattern for improved facial expression recognition - Suraiya, Y.; Pathan, R. K.; Biswas, M.; Khandaker, Mayeen Uddin *; Faruque, M. R. I. (MDPI, 2020)
Compelling facial expression recognition (FER) processes have been utilized in very successful ﬁelds like computer vision, robotics, artiﬁcial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to diﬀerent scales that can aﬀect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1)and LBP(8,2)and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The ﬁltered images then go through the feature extraction method and wait for the classiﬁcation process. Four machine learning classiﬁers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade(CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces(KDEF)dataset.