Facial Expression Recognition Method with Attention-Free Capsule Network

被引：0

作者：

Xu, Xuebin ^{[1
,2
]}

Liu, Chenguang ^{[1
,2
]}

Lu, Longbin ^{[1
]}

Cao, Shuxin ^{[1
]}

Xu, Zongyu ^{[1
]}

机构：

[1] School of Computer Science, Xi’an University of Posts & Telecommunications, Xi’an

[2] Shannxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts & Telecommunications, Xi’an

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 22期

关键词：

capsule neural network; deep learning; facial expression recognition; sMLP-CapsNet; spare multilayer perceptron（sMLP）;

D O I：

10.3778/j.issn.1002-8331.2206-0329

中图分类号：

学科分类号：

摘要：

Expression recognition technology can analyze the emotional activities of recognized objects from human expressions. Aiming at the problem that an effective feature extraction and mapping model cannot be established due to the complex spatial relationship and feature information in facial expression images, the spare multilayer perceptron （sMLP）uses a small amount of parameters to allow each spatial location to perform, and the capsule network can also express the spatial pose information of the feature, so this paper proposes a new facial expression recognition model sMLP-CapsNet to improve the ability of expression recognition spatial relationship mapping. The CK+ dataset and the RAF-DB dataset are used to extract facial expression picture features from contour to detail by using an improved capsule neural network to achieve facial expression classification. Compared with other facial expression recognition algorithms, the accuracy of the model is significantly improved, and the recognition rates on the CK+ dataset and RAF-DB dataset can reach 99.48% and 85.69%, respectively, showing the advanced nature of the algorithm. © The Author(s) 2023.

引用

页码：251 / 258

页数：7

共 44 条

[1]

HE J, BAXTER S L, Et al., The practical implementation of artificial intelligence technologies in medicine[J], Nature Medicine, 25, 1, pp. 30-36, (2019)

[2]

TIAN Y，, KANADE T, COHN J F., Facial expression recognition，and handbook of face recognition[M], pp. 487-519, (2011)

[3]

LI R X, CAI Z X, WANG B B, Et al., Continuous emotion recognition method based on face image and EEG[J], Computer Systems & Applications, 30, 2, pp. 1-11, (2021)

[4]

EKMAN P, FRIESEN W V., Constants across cultures in the face and emotion[J], Journal of Personality and Social Psychology, 17, 2, pp. 124-129, (1971)

[5]

MATSUMOTO D., More evidence for the universality of a contempt expression[J], Motivation and Emotion, 16, 4, pp. 363-368, (1992)

[6]

NG P C，, HENIKOFF S., SIFT：predicting amino acid changes that affect protein function[J], Nucleic Acids Research, 31, 13, pp. 3812-3814, (2003)

[7]

BAY H, TUYTELAARS T, GOOL L V., SURF：speeded up robust features[C], Proceedings of the 9th European Conference on Computer Vision, pp. 404-417, (2006)

[8]

DALAL N, TRIGGS B., Histograms of oriented gradients for human detection[C], Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886-893, (2005)

[9]

OJALA T, PIETIKAINEN M, MAENPAA T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 7, pp. 971-987, (2002)

[10]

LIU K, ZHANG M, PAN Z., Facial expression recognition with CNN ensemble[C], Proceedings of the 2016 International Conference on Cyberworlds, pp. 163-166, (2016)

← 1 2 3 4 5 →