Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Issue title: Cross-domain Applications of Fuzzy Logic and Machine Learning
Guest editors: Ekaterina Isaeva and Álvaro Rocha
Article type: Research Article
Authors: Yan, Lixin; *
Affiliations: Department of Police training, Liaoning Police College, Liaoning, Dalian, China
Correspondence: [*] Corresponding author. Lixin Yan, Department of Police training, Liaoning Police College, Liaoning, Dalian, China. E-mail: yanlixin517@sina.com.
Abstract: In traditional teaching, people can quickly guess the emotion type of the other person based on facial expressions but understanding the human facial expression by computer is a very complicated problem. There may be some differences in the expression of each person. Therefore, how to make the computer ‘read and understand’ the emotional state of the person according to the facial expression of the person is the research focus of this paper. In this paper, the expression recognition based on dynamic sequence is developed, and the mapping relationship between basic expression and emotion is studied to construct the emotional model, and the emotional state recognition of the learner is realized by the research on facial expression. In the intelligent teaching environment, the teacher adjusts the teaching strategy according to the emotional state recognition test results, improves the teaching efficiency, and realizes the wisdom teaching. Moreover, combined with the actual situation, an expression recognition algorithm based on the ultra-wide regression network model for unsupervised learning classroom education is constructed. Through experimental analysis, we can know that this research algorithm has certain advantages in facial expression recognition.
Keywords: Ultra-wide regression network, unsupervised learning, classroom education, expression recognition, feature extraction
DOI: 10.3233/JIFS-179794
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 6, pp. 7167-7177, 2020
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl