Expression Recognition Method Combined with Attention Feature Fusion

doi:10.15966/j.cnki.dnydx.2024.05.016

Computer & Telecommunication

2024, Vol. 1

Issue (5): 71- DOI: 10.15966/j.cnki.dnydx.2024.05.016

Current Issue | Archive | Adv Search

Expression Recognition Method Combined with Attention Feature Fusion

Guizhou Normal University

Download:
Export: BibTeX | EndNote (RIS)

Abstract Aiming at the problems of insufficient expression of facial expression features, low recognition accuracy and many pa‐ rameters, an octave convolutional expression recognition method combined with attention feature fusion is proposed. The main inno‐ vation point is to introduce the attention feature fusion mechanism into the model to optimize the fusion of different scale features. Deep separable network is used to replace traditional convolution, which greatly reduces parameters. BN and PReLU are introduced to improve the stability and performance of the model. Experiments show that the accuracy of the model on CK+ and Fer2013 data sets is 98.91% and 74.03%, respectively, showing excellent generalization ability and accuracy.

Key words： facial expression recognition convolutional neural network attention feature fusion mechanism

Published: 12 October 2024

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

REN Hao. Expression Recognition Method Combined with Attention Feature Fusion. Computer & Telecommunication, 2024, 1(5): 71-.

URL:

https://www.computertelecom.com.cn/EN/10.15966/j.cnki.dnydx.2024.05.016 OR https://www.computertelecom.com.cn/EN/Y2024/V1/I5/71

[1]	LI Chun-hui WANG Xiao-ying ZHANG Qing-jie LIU Han-zhuo LIANG Jia-ye GAO Ning-kang. DDoS Attack Detection Method Based on Multi-scale Convolutional Neural Network [J]. 电脑与电信, 2024, 1(6): 35-.
[2]	WANG Jin WANG Rui. Facial Expression Recognition Algorithm Based on Multi-scale Feature Deep Learning[J]. 电脑与电信, 2024, 1(5): 75-.
[3]	SU Cui-wen CHAI Guo-qiang. Real-time Facial Expression Recognition Based on Facial Feature Detection [J]. 电脑与电信, 2023, 1(1-2): 17-21.
[4]	MA Jin-feng. Facial Expression Recognition Based on Dense Connected Convolution Structure[J]. 电脑与电信, 2021, 1(4): 1-5.
[5]	ZHANG Bao-yan. Research on Geographic Image Classification Based on Deep Convolution Network [J]. 电脑与电信, 2021, 0(10): 75-79.
[6]	JU Cong LI Tao. Research on Facial Expression Recognition Based on the Depthwise Separable Convolution Structure[J]. 电脑与电信, 2020, 1(6): 1-5.
[7]	ZHANG Gang , CHEN Jia-lian, SONG Jian, GUO Jun-qi, ZHOU Chen-rui. Chinese Landscape Painting Automated Generation Model Based on Generative Adversarial Networks[J]. 电脑与电信, 2020, 1(3): 1-.
[8]	XIE Shumin DENG Lei XIE Zhong-mei LI Hai-ping. DenoisingAlgorithm Based on Convolutional Neural Network in Real Scene[J]. 电脑与电信, 2020, 1(11): 39-43.
[9]	LinWeisheng. The Application of Deep Learning Technologies in Data Analysis of Information System[J]. 电脑与电信, 2017, 1(6): 51-53.
[10]	Li Senlin, Peng Xiaoning. Application of Deep CNN in the Class Status of Students[J]. 电脑与电信, 2017, 1(10): 35-37.

Viewed

Full text

Abstract

Cited

Shared

Discussed