Facial expression recognition method based on deep convolutional neural network combined with improved LBP features
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,Computer Science Applications,Hardware and Architecture
Link
http://link.springer.com/content/pdf/10.1007/s00779-019-01238-9.pdf
Reference20 articles.
1. Zheng W (2014) Multi-view facial expression recognition based on group sparse reduced-rank regression. IEEE Trans Affect Comput 5(1):71–85
2. Zhao X, Shi X, Zhang S (2015) Facial expression recognition via deep learning. IETE Tech Rev 32(5):347–355
3. Boughrara H, Chtourou M, Amar CB et al (2016) Facial expression recognition based on a mlp neural network using constructive training algorithm[J]. Multimed Tools Appl 75(2):709–731
4. Guo Y, Zhao G, Pietikainen M (2016) Dynamic facial expression recognition with atlas construction and sparse representation. IEEE Trans Image Process 25(5):1977–1992
5. Li W, Ke W, Li R (2015) Unsupervised feature selection based on spectral regression from manifold learning for facial expression recognition. Computer Vision Iet 9(5):655–662
Cited by 31 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. An analysis of machine learning techniques for multimodal recognition;2024 International Research Conference on Smart Computing and Systems Engineering (SCSE);2024-04-04
2. Faster Region Convolutional Neural Network (FRCNN) Based Facial Emotion Recognition;Computers, Materials & Continua;2024
3. Intelligent and Small Samples Gear Fault Detection Based on Wavelet Analysis and Improved CNN;Processes;2023-10-13
4. A dual‐channel ensembled deep convolutional neural network for facial expression recognition in the wild;Computational Intelligence;2023-06-06
5. A comprehensive survey on deep facial expression recognition: challenges, applications, and future guidelines;Alexandria Engineering Journal;2023-04
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3