Emotion Classification Through Facial Expressions Using SVM and Convolutional Neural Classifier

Abstract

Emotions being an influential personal state of feelings, have phenomenal importance, and facial expressions are one’s instinctive reflections and photoprints of emotions. These facial expressions that count to be 55% of the total human communication cannot remain unnoticed, especially in today’s expanding world of Human-Computer interaction, where the need of the hour is to train computers to recognize human emotions from facial expressions of images. Four models are developed in this work for emotion classification. This work utilizes HOG (descriptor) and SVM for the first model while employing CNN models with varying input strategies with and without down-sampling in the remaining three models to predict the given FER dataset images into either of the seven universal facial expressions. The first model extracts the histogram of oriented gradient (HOG) from the images and applies classification with a support vector machine (SVM). The second model inputs raw pixel image data for training. The third model uses a novel hybrid feature strategy that maneuvers a combination of HOG features and pixel data of images. The last model uses the same architecture as the previous two CNN models but with a balanced dataset (all classes having the same number of images). Batch normalization, dropout, and L2 regularization reduced the overfitting of models, and the GPU improved the training speed. The hybrid technique (Model-3) performed better than model-1, model-2, and model-4 in terms of accuracy and F1 score. The performance evaluation speaks about the falter arising with downsampling in model-4.