NDLI: Two-dimensional discriminant multi-manifolds locality preserving projection for facial expression recognition

Content Provider	IEEE Xplore Digital Library
Author	Ning Zheng Xin Guo Lin Qi Ling Guan
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. & Eng., Zhengzhou Univ., Zhengzhou, China (Ning Zheng; Xin Guo; Lin Qi) \|\| Ryerson Multimedia Res. Lab., Ryerson Univ., Toronto, ON, Canada (Ling Guan)
Abstract	In this paper, we assume that samples of different expressions reside on different manifolds and propose a novel human emotion recognition framework named two-dimensional discriminant multi-manifolds locality preserving projection (2D-DMLPP). 2D-DMLPP focuses on salient regions which reflect the significant variation from facial expression images so that it can learn an expression-specific model from salient patches rather than that of subject-specific. Furthermore, conventional manifold learning methods ignore the variation among nearby samples from the same class, leading to serious overfitting. We construct three adjacency graphs to model the margin and information, including diversity and similarity of salient patches from the same expression, and then incorporate the information and margin into dimensionality reduction function. Several experiments show that the proposed method significantly improves the recognition performance of facial expression recognition.
Starting Page	2065
Ending Page	2068
File Size	762740
Page Count	4
File Format	PDF
ISBN	9781479983919
DOI	10.1109/ISCAS.2015.7169084
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-24
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Feature extraction Manifolds Databases Diversity reception Emotion recognition Training facial expression recognition manifold learning two-dimensional multi-manifolds discriminant locality preserving projection (2D-DMLPP) feature extraction
Content Type	Text
Resource Type	Article

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