Facial Expression Recognition Using Deep Siamese Neural Networks with a Supervised Loss function

摘要

This paper presents a novel algorithm for an end-to-end facial expression recognition(FER) system based on deep Siamese neural networks equipped with a supervised loss function. Our method learns a powerful FER system by dynamically modulating verification signal over identification/classification signal. The identification signal increases the inter-class variations by maximizing the distance between the features for different classes, while the verification signal reduces the intra-class variations by minimizing the distance between features for the same class. We have evaluated our method on the AffectNet dataset [10] and achieved promising results compared to other deep learning models.