Open AccessUnsupervised feature selection based on spectral regression from manifold learning for facial expression recognition
Author(s) -
Wang Li,
Wang Ke,
Li Ruifeng
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0278
Subject(s) - pattern recognition (psychology) , artificial intelligence , feature selection , feature (linguistics) , regression , computer science , benchmark (surveying) , facial expression , graph , regression analysis , feature vector , mathematics , feature extraction , facial recognition system , machine learning , statistics , philosophy , linguistics , geodesy , theoretical computer science , geography
In this study, an unsupervised feature selection method is proposed for facial feature recognition (FER) in the absence of class labels. The contribution is the descriptive feature components selector spectral regression representative coefficient scores based on graph manifold learning from high‐dimensional feature space. The spectral regression analysis and L1‐regularised least square are then used to compute the importance of features in the original space, so that less representative features with lower coefficient scores will be removed without prior distribution assumption. To verify the performance of the authors’ method, some classifiers are used to classify facial expressions on three benchmark facial expression databases. The recognition results indicate the availability and effectiveness of the proposed method for FER.