Unsupervised feature selection based on spectral regression from manifold learning for facial expression recognition

Li Wang; Ke Wang; Ruifeng Li

Unsupervised feature selection based on spectral regression from manifold learning for facial expression recognition

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Author(s): Li Wang ¹ ; Ke Wang ¹ ; Ruifeng Li ¹
- Affiliations: 1: Science Park of Harbin Institute of Technology, Room 305, Building C1, Yikuang Street, Nangang District, Heilongjiang Province, Harbin 150080, People's Republic of China
Source: Volume 9, Issue 5, October 2015, p. 655 – 662
DOI: 10.1049/iet-cvi.2014.0278 , Print ISSN 1751-9632, Online ISSN 1751-9640

Received 22/08/2014, Accepted 02/06/2015, Revised 22/05/2015, Published 14/08/2015

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.

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Unsupervised feature selection based on spectral regression from manifold learning for facial expression recognition

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