Human Emotion Recognition by Facial Expression Using Modified EfficientNet-B3 Model

Abstract

Human emotion plays a critical role in the purpose of communication, with the correct detection of emotion, one can analyze the human feeling without even asking, as it is non-verbal communication method. Human emotion through facial expression is one of the highlighted topics of study because of its wide range of application from robotics, security, artificial intelligence, marketing and health monitoring where human-computer interaction (HCI) is a key ingredient. To tackle the challenges that arise in accurate human facial emotion recognition, a modified version of the EfficientNet-B3 model is proposed, which shows promising results for emotion detection using the FER-2013 dataset. By leveraging the universal FER-2013 dataset, which contains more than 35 000 grey-scale human facial images, the proposed model aims to improve recognition performance. The modified architecture incorporates EfficientNet-B3 as the base model, with an additional batch normalization layer followed by a dense layer and an output layer. The model has been trained on the facial dataset while considering these crucial factors. Performance evaluation of the model is conducted using confusion matrix, precision, recall, accuracy, and F1 score as performance metrics. Remarkably, the proposed model achieved an impressive accuracy of 92 % for training and 83.0 % for validation of the dataset. This implies that the proposed model yields highly accurate emotion recognition with a given dataset and can provide improvement in the overall efficiency of emotional recognition applications, particularly in human-to-machine interactions.