A CNN-Based Facial Expression Recognition System

Abstract

Facial expression recognition has long attracted researchers from a variety of domains, such as human-computer interaction, emotion analysis, intelligent medical care, and so on. Various human-designed features have been used in FER to extract image appearance features, but human-designed methods are difficult to be used to recognize new face images, which poses challenges for FER in uncontrolled environments. Recently, CNNs and FNNs have been used for facial expression recognition. The new CNN-based recognition approach yields excellent FER results. The numerous convolution and pooling layers that CNN possesses allow it to extract higher and multi-level features of the entire face or local regions. Additionally, CNN has excellent classification performance when it comes to the feature extraction of facial expression pictures. However, the mechanism through which FNN remains largely controversial. In this work, the experiments are carried out on the comparison of three facial expression recognition algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), and the experimental results show that, using CNN can get more stable recommendation results of better quality.