Deep Sparse Autoencoder Network for Facial Emotion Recognition

Luefeng Chen; Min Wu; Witold Pedrycz; Kaoru Hirota

doi:10.1007/978-3-030-61577-2_3

Deep Sparse Autoencoder Network for Facial Emotion Recognition

Luefeng Chen^*, Min Wu, Witold Pedrycz, Kaoru Hirota

^*此作品的通讯作者

科研成果: 书/报告/会议事项章节 › 章节 › 同行评审

4 引用（Scopus）

摘要

Deep neural network (DNN) has been used as a learning model for modeling the hierarchical architecture of human brain. However, DNN suffers from problems of learning efficiency and computational complexity.

源语言	英语
主期刊名	Studies in Computational Intelligence
出版商	Springer Science and Business Media Deutschland GmbH
页	25-39
页数	15
DOI	https://doi.org/10.1007/978-3-030-61577-2_3
出版状态	已出版 - 2021
已对外发布	是

出版系列

姓名	Studies in Computational Intelligence
卷	926
ISSN（印刷版）	1860-949X
ISSN（电子版）	1860-9503

访问文件

10.1007/978-3-030-61577-2_3

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引用此

Chen, L., Wu, M., Pedrycz, W., & Hirota, K. (2021). Deep Sparse Autoencoder Network for Facial Emotion Recognition. 在 Studies in Computational Intelligence (页码 25-39). (Studies in Computational Intelligence; 卷 926). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-61577-2_3

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abstract = "Deep neural network (DNN) has been used as a learning model for modeling the hierarchical architecture of human brain. However, DNN suffers from problems of learning efficiency and computational complexity.",

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AU - Wu, Min

AU - Pedrycz, Witold

AU - Hirota, Kaoru

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N2 - Deep neural network (DNN) has been used as a learning model for modeling the hierarchical architecture of human brain. However, DNN suffers from problems of learning efficiency and computational complexity.

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BT - Studies in Computational Intelligence

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Deep Sparse Autoencoder Network for Facial Emotion Recognition

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