Facial emotions recognition using local monotonic pattern and grey level co‐occurrence matrices images aided development.

In this article, local monotonic pattern (LMP) paired with grey level co‐occurrence matrix (GLCM) methods are suggested to identify facial emotions with a high identification rate even when the face pictures are rotated. The proposed method extracts image features using the properties of the LMP algorithm and features extracted from the GLCM, which are then fed into the Support Vector Machine (SVM) process that reduces the dimensionality of the features vector and classifies the output into different facial expressions or emotions. The SVM performance rate is then compared to the K‐nearest neighbour approach (KNN) to see which method produces the best facial emotion identification and categorization. The study identified facial emotions in the images using advanced algorithms of GLCM and LMP models to be compared. As a result, the accuracy of SVM and KNN was utilized to determine the method's usefulness in classification using the application of MATLAB. A result of more than 93% was achieved using the SVM method compared with 89.4% using the KNN for the recognition process. The study also demonstrated that this approach would lead to more classification outcomes if the LMP and GLCM are combined with an edge‐based technique yielding a new method that is more efficient and more effective. [ABSTRACT FROM AUTHOR]