CNN Learning Strategy for Recognizing Facial Expressions

Abstract

The ability to recognize facial expressions using computer vision is a crucial task, with numerous potential applications. Although deep neural networks have achieved high performance, it is still difficult to use them in the recognition of facial expressions. This is because different facial expressions have varying degrees of similarities among themselves, and numerous variations cause diversity in the same facial images. In this study, we propose a novel divide-and-conquer-based learning strategy to improve the performance of facial expression recognition (FER). The face area in an image was detected using MobileNet, and a ResNet-18 model was used as a backbone deep neural network for recognizing facial expressions. Thereafter, groups that included similar facial expressions were categorized by analyzing the confusion matrix, which is the inference result of the trained ResNet-18 model, and these similar facial expression groups were utilized to re-train the deep learning model. In the experiments, the proposed method was evaluated using two thermal (Tufts and RWTH) and two RGB (RAF and FER2013) datasets, and higher FER performances, which were 97.75% for Tufts, 86.11% for RWTH, 90.81% for RAF, and 77.83% for FER2013, were achieved. As such, the proposed method can accurately classify large amounts of facial expression data.

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