Integrating Hybrid Feature Extraction Techniques with Support Vector Machine for Efficient Facial Emotion Recognition

Facial emotion recognition is a vital piece of brain science, criminology, and web-based media. In every one of these fields, a serious level of unwavering quality of order is basic. Presently human judgment is utilized in these fields however people are not generally precise. Subsequently, there is a requirement for a solid and speedy method of distinguishing human feelings. The new advances in AI and example acknowledgment have offered a few calculations to perceive human feelings like Local Binary Pattern (LBP), Convolutional Neural Network (CNN), GLCM for feature extraction, and SVM for Classifier. A facial emotion recognition software that detects five basic emotions of a face is implemented in this paper. We had learned many machine learning technologies and algorithms. Along with that, we learned technologies like sci-kit-learn, Keras, OpenCV that were used in the implementation of our paper. In this paper, we used technologies like LBP, GLCM, and Gabor filter to extract aspects of images. For training the model, we used the CK + dataset that contains 24,282 images in the training set. In this, Support Vector Machine (SVM) is applied for the classification of emotions using the features extracted from the above technologies.