Facial Expression Recognition based on ArcFace Features and TinySiamese Network

Facial Expressions Recognition (FER) has become an active area of research. To accomplish emotion recognition, several of machine learning algorithms have been employed. However, these models require a significant amount of data, a large training memory and an important time for training. This paper presents a Facial Expression Recognition method based on ArcFace features and TinySiamese network to solve these problems. The proposed method consists of two parts: the first part is for feature extraction and the second is for feature recognition. In the first part, a feature extractor was used to extract features from the embedded image. The feature extractor was based on a ResNet-50 network and ArcFace loss function. In the second part, the TinySiamese network was used for feature classification and verification. The proposed method was evaluated on three popular datasets: FER2013, RAF-DS, and ExpW. The experimental part shows that the proposed method achieved competitive results compared to related works with classification rates equal to 60.43%, 85.14%, 65.13% and verification rates equal to 73.31%, 84.29%, 76.08% on the FER2013, RAF-DS and ExpW datasets respectively.