Exponential elastic preserving projections for facial expression recognition

NEUROCOMPUTING

卷:275

Article

Yuan, Sen¹  Mao, Xia¹ 

[1] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China

关键词: Facial expression recognition;    Dimensionality reduction;    Small sample size problem;    Manifold learning;    Matrix exponential;   

DOI  :  10.1016/j.neucom.2017.08.067

来源: Elsevier

【 摘 要 】

As a typical manifold learning method, elastic preserving projections (EPP) can well preserve the local geometry and the global information of the training set. However, EPP generally suffers from two issues: (1) the algorithm encounters the well known small sample size (SSS) problem; (2) the algorithm is based on the adjacent graph such that it is sensitive to the size of neighbors. To address these problems, we propose a novel method called exponential elastic preserving projections (EEPP), principally for facial expression recognition. By utilizing the properties of matrix exponential, EEPP is not only able to exploit the manifold structure of data, but also can get rid of the issues mentioned above. Experiments conducted on the synthesized data and several benchmark databases illustrate the effectiveness of our proposed algorithm. (C) 2017 Elsevier B. V. All rights reserved.

【 授权许可】

Free