NDLI: Modified classification and regression tree for facial expression recognition with using difference expression images

Content Provider	IET Digital Library
Author	Du, Lingshuang Hu, Haifeng
Abstract	This study presents a modified classification and regression tree (M-CRT) framework based on difference expression images, to address the facial expression recognition (FER) problem. The authors firstly obtain facial expressional details by calculating the difference between the images of basic expressions and images of neutral expression, which reflect the information irrelevant to identities. Local binary patterns and supervised descent method are, respectively, used to obtain the global and local features from difference expression images. M-CRT model is developed for FER, which uses recursive segmentation to find the best classification decision according to the attributes of the global and local features, respectively. Compared with traditional methods, M-CRT can simultaneously maximise intra-class purity and distance between classes, which improves the discriminating power for classification. Experimental results on Japanese Female Facial Expression and CK+ database verify the effectiveness of their method.
Starting Page	590
Ending Page	592
Page Count	3
ISSN	00135194
Volume Number	53
e-ISSN	1350911X
Issue Number	Issue 9, Apr (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/53/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2017.0731
Journal	Electronics Letters
Publisher Date	2017-03-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CK+ Database Combinatorial Mathematics Computer Vision And Image Processing Technique Difference Expression Image Emotion Recognition Face Recognition Facial Expression Recognition FER Image Classification Image Recognition Image Segmentation Intra-class Purity Japanese Female Facial Expression Local Binary Pattern M-CRT Model Mathematics Modified Classification And Regression Tree Framework Recursive Segmentation Regression Analysis Supervised Descent Method Trees
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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