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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	Studies in Computational Intelligence
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	25-39
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-030-61577-2_3
Publication status	Published - 2021
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	926
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

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

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Chen, L., Wu, M., Pedrycz, W., & Hirota, K. (2021). Deep Sparse Autoencoder Network for Facial Emotion Recognition. In Studies in Computational Intelligence (pp. 25-39). (Studies in Computational Intelligence; Vol. 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.",

author = "Luefeng Chen and Min Wu and Witold Pedrycz and Kaoru Hirota",

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T1 - Deep Sparse Autoencoder Network for Facial Emotion Recognition

AU - Chen, Luefeng

AU - Wu, Min

AU - Pedrycz, Witold

AU - Hirota, Kaoru

PY - 2021

Y1 - 2021

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.

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

M3 - Chapter

AN - SCOPUS:85096210139

T3 - Studies in Computational Intelligence

SP - 25

EP - 39

BT - Studies in Computational Intelligence

PB - Springer Science and Business Media Deutschland GmbH

ER -

Deep Sparse Autoencoder Network for Facial Emotion Recognition

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