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.
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Cui, R., Plested, J., Liu, J. (2020). Declarative Residual Network for Robust Facial Expression Recognition. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_39
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DOI: https://doi.org/10.1007/978-3-030-63820-7_39
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