CVGG-19: Customized Visual Geometry Group Deep Learning Architecture for Facial Emotion Recognition

Abstract

Facial emotion recognition (FER) detects a user's facial expression with the camera sensors and behaves according to the user's emotions. The FER can apply to entertainment, security, and traffic safety. The FER system requires a highly accurate and efficient algorithm to classify the driver's emotions. The-state-of-art architectures for FER, such as visual geometry group (VGG), Inception-V1, ResNet, and Xception, have some level of performance for classification. Nevertheless, the original VGG architectures suffer from the vanishing gradient, limited improvement performance, and expensive computational cost. In this paper, we propose the customized visual geometry group-19 (CVGG-19), which adopts the designs of the VGG, Inception-v1, ResNet, and Xception. Our proposed CVGG-19 architecture outperforms the conventional VGG-19 architecture by 59.29%, reducing the computational cost by 89.5%. Moreover, the CVGG-19 architecture's F1-score, which represents the real-time classifying performance, displays superior to the Inception-V1, ResNet50, and Xception architectures by 3.86% on average