Abstract
Facial expression is one of the most representative signals for human emotional states and intentions. Facial expression recognition has attracted increasing attention in academia and industry, and has been wide used in robotics, intelligent security, medical monitoring, educational evaluation, driving fatigue monitoring, etc. This paper proposes a lightweight network Dense-MobileNet. In which, a DenseDW-block for feature reuse is designed and embedded into MobileNetV1 for better accuracy and less computation. The width selection and comparison experiments are used on the widely used Real-World Affective Face Database (RAF-DB) to choose the best network parameters and to validate the effectiveness of the proposed Dense-MobileNet. The results show that: 1) Among the three proposed sub networks Dense-MobileNet-1, Dense-MobileNet-2, Dense-MobileNet-3, the Dense-MobileNet-2 has the best accuracy of 82.4%. 2) Comparing with MobileNetV1, the recognition accuracy of our model is improved by 2.5%, the number of parameters is reduced by 45.7%, and the amount of computation is reduced by 66.73%. As a lightweight network with better accuracy and less computation, the proposed Dense-MobileNet is suitable for facial expression recognition on mobile terminals and edge devices. The proposed DenseDW-block serving as a feature reuse module can be used to design or optimize similar CNN to improve accuracy and accelerate computation.
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This research was supported by the National Key R&D Program of China under Grant No. 2020YFB1707700.
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Xu, X., Tao, R., Feng, X., Zhu, M. (2022). A Lightweight Facial Expression Recognition Network Based on Dense Connections. In: Uden, L., Ting, IH., Feldmann, B. (eds) Knowledge Management in Organisations. KMO 2022. Communications in Computer and Information Science, vol 1593. Springer, Cham. https://doi.org/10.1007/978-3-031-07920-7_27
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DOI: https://doi.org/10.1007/978-3-031-07920-7_27
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