Facial Expression Recognition Based on Sparse Locality Preserving Projection

Jingjie YAN
Wenming ZHENG
Minghai XIN
Jingwei YAN

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E97-A    No.7    pp.1650-1653
Publication Date: 2014/07/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E97.A.1650
Type of Manuscript: LETTER
Category: Image
Facial expression recognition,  Sparse locality preserving projection (SLPP),  Feature selection,  

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In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.