Image Processing for Facial Expression using Machine Learning Algorithm

Abstract

Visual detections of face expression have become much difficult job and yields in reduced preciseness against the automated face expression identification making use of image processing for emotion identification and take lesser time and less efforts with increased accurate outcomes. Effective identification and classification of face-based expressions are found to be beneficial in people's behavioral monitoring system. This paper presents detailed texture bound analyzes architecture framework and codebook generation for face expression detection and classification method. The proposed system acquires Local threshold-texture Patterns (LT - TP) from input face images of unique classes. The features from LT - TP were effective and the method was able to acquire discriminant data and indicate every class containing less dimensions in it. Then, unique codebooks are generated for each class of images. Finally, Classification is done by making use of multiclass typed SVM-Support Vector Machines. Experimentations are performed on Matlab on face dataset that contains minimum two unique classes namely normal and happy faces.