Analysis and Comparison of Machine Learning-Based Facial Expression Recognition Algorithms

Abstract

With the rapid development of artificial intelligence technology, facial expression recognition (FER) has gained increasingly widespread applications in digital human generation, humanoid robotics, mental health, and human–computer dialogue. Typical FER algorithms based on machine learning have been widely studied over the past few decades, which motivated our survey. In this study, we have surveyed the state of the art in FER across two categories: traditional machine learning-based (ML-based) and deep learning-based (DL-based) approaches. Each category is analyzed based on six subcategories. Then, twelve methods, including four ML-based models and eight DL-based models, are compared to evaluate FER performance across four datasets. The experimental results show that in validation sets, the average accuracy of HOG-SVM is 50.12%, which is the best performance for the four ML-based methods; in contrast, Poster has an average accuracy of 75.98%, which is the best result obtained among the eight DL-based methods. The most difficult expression to recognize is contempt, with recognition accuracies of 10.00% and 40.06% for ML-based and DL-based methods, respectively. The accuracy of the ML-based method for identifying neutral expression is the highest at 35.25%; the DL-based method has the highest accuracy in identifying surprise at 69.56%. From the theoretical analysis and comparative experimental results of existing methods, we can see that FER faces challenges, including inaccurate recognition in complex environments and unbalanced data categories, highlighting several future research directions, especially those involving the latest applications of digital humans and large language models.