Abstract
Facial expression recognition is a significant direction in facial computer version. Although convolutional neural networks (CNNs) have received great attention in recognition task especially for images, they require considerable time in computation and are easily to be trapped in over-fitting due to kinds of reasons. This paper suggests a fast and efficient network for expression recognition, which takes full advantages of CNN and ELM (Extreme Learning Machine). Facial expressions can be learned well and calculated fast with satisfying accuracy through it. Experimental results on real-life expression database prove that our proposed approach can effectively reduce the calculation time and improve the performance.
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Acknowledgments
This work is supported by National Natural Science Foundation (NNSF) of China under Grant No. 61433003, 61973036.
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Zou, Y., Ren, X. (2020). Efficient and Fast Expression Recognition with Deep Learning CNN-ELM. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_35
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DOI: https://doi.org/10.1007/978-981-32-9682-4_35
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