Deep Siamese Neural Networks for Facial Expression Recognition in the Wild

Abstract

This paper introduces an algorithm for Facial Expression Recognition (FER) using deep Siamese Neural Networks (SNNs) that preserve the local structure of images in the embedding similarity space. We designed the network to reveal the input pairs similarity by comparing features through a designed metric. Furthermore, we developed a novel image pairing (i.e., positive and negative pairs) strategy technique to train our Siamese model. Our Siamese model comprises of a verification framework and an identification framework to learn a joint embedding space. The verification path reduces the intra-class variations by minimizing the distance between the extracted features from the same class, while the identification path increases the inter-class variations by maximizing the distance between the features extracted from different classes. We apply transfer learning to only use the identification model for facial expression classification. We evaluated our algorithm using AffectNet, FER2013, and Compound Facial Expressions of Emotion (CFEE) datasets, where better results are achieved compared to other deep learning-based approaches.