Declarative Residual Network for Robust Facial Expression Recognition

Ruikai Cui, Josephine Plested, Jiaxu Liu

Abstract: Automatic facial expression recognition is of great importance for the use of human-computer interaction (HCI) in various applications. Due to the large variance in terms of head position, age range, illumination, etc, detecting and recognizing human facial expressions in realistic environments remains a challenging task. In recent years, deep neural networks have started being used in this task and demonstrated state-of-the-art performance. Here we propose a reliable framework for robust facial expression recognition. The basic architecture for our framework is ResNet-18, in combination with a declarative $$L_p$$ sphere/ball projection layer. The proposed framework also contains data augmentation, voting mechanism, and a YOLO based face detection module. The performance of our proposed framework is evaluated on a semi-natural static facial expression dataset Static Facial Expressions in the Wild (SFEW), which contains over 800 images extracted from movies. Results show excellent performance with an averaged test accuracy of $$51.89\%$$ for five runs, which indicates the considerable potential of our framework.