Facial Expression Recognition Using Deep Neural Network

Facial Expression Recognition (FER) system analyzes a person's emotions using facial expressions. Nonverbal communication cues, such as facial expressions and gestures, can be seen in FER. FER has applications in various areas, such as medical diagnosis and treatments, human resources, police investigation, education, customer service, journalism, and more. Nowadays, Convolutional Neural Networks (CNNs) play an important role due to their inherent feature extraction mechanism from images. This work utilizes a CNN to classify facial expressions into seven classes: anger, disgust, fear, happiness, sadness, surprise, and neutral. Experiments are conducted with different CNN models and pre-trained models on the FER-2013 dataset. We explore how the model accuracy changes with the model parameters and compare our classification results with existing CNNs. The experimental results demonstrate that the proposed CNN model, trained on a balanced dataset with data augmentation and batch normalization, achieves a training accuracy of 80% and a test accuracy of 72%. Furthermore, the experimental results show that EfficientNet-B1 can achieve a training accuracy of 93% and a test accuracy of 86% on a smaller subset of the dataset containing three emotion classes, which is more accurate than the state-of-the-art models.