Open AccessModified classification and regression tree for facial expression recognition with using difference expression images
Author(s) -
Du Lingshuang,
Hu Haifeng
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.0731
Subject(s) - pattern recognition (psychology) , artificial intelligence , expression (computer science) , segmentation , facial expression , decision tree , tree (set theory) , regression , class (philosophy) , computer science , facial expression recognition , mathematics , facial recognition system , statistics , programming language , mathematical analysis
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.