A Comparative Evaluation of CNN and Deep Learning Models for Facial Emotion Recogni-tion in Affective Healthcare
TL;DR
A comparative evaluation of machine learning and deep learning models for the prediction of face emotion using the CK+ dataset proposed by Cohn-Kanade shows high accuracy achieved by the CNN model, while the SVM classifier achieved a testing accuracy of 99%.
Abstract
Facial expression prediction has gained considerable attention in recent years, particularly because of its healthcare related applications in human-computer interaction. This paper presents a comparative evaluation of machine learning and deep learning models for the prediction of face emotion using the CK+ dataset proposed by Cohn-Kanade. The dataset is characterized by seven classes of emotions rep-resented by the labels, namely surprise, happiness, disgust, anger, sadness, fear, and contempt, on 784 training, 98 validation, and 99 testing images. To further improve model performance, preprocessing techniques were employed that enhanced data efficiency. To increase variability in the data and reduce overfitting, all images were scaled to a 48*48-pixel resolution, pixel values were scaled to be between 0 and 1 for uniformity and the following data augmentation techniques were implemented: 10-degree rotation, horizontal flip, 0.15 zoom. The five models that were tested were CNN, SVM, VGG16, InceptionV3 and VGG19. The results demonstrate the high accuracy achieved by the CNN model which showed an accuracy of 98.98%, 99% and 99% in training, validation and test respectively. The SVM classifier achieved a testing accuracy of 99%. Both InceptionV3 and VGG19 on the other hand achieved competitive testing accuracy values of 90.91% and 97.98% respectively, while VGG16 got tested and reached an accuracy of 85.86%.