Improved Xception with Local Dense Connections and Transition Layer for Facial Expression Recognition

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Improved Xception with Local Dense Connections and Transition Layer for Facial Expression Recognition

Traditional deep convolutional neural networks are used for facial expression recognition, which makes the number of neurons and parameters huge, wastes computing resources, and even causes problems such as overfitting and network degradation. Meanwhile, single-scale expression features cannot descr...

書誌詳細
出版年:	Proceedings of 2024 3rd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024
第一著者:	2-s2.0-85219174176
フォーマット:	Conference paper
言語:	English
出版事項:	Association for Computing Machinery, Inc 2025
オンライン･アクセス:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219174176&doi=10.1145%2f3707292.3707398&partnerID=40&md5=1f7308a3da4526322d8aa6a17186e516

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