Title :
Orthogonal linear local spline discriminant embedding for face recognition
Author :
Lei, Ying-Ke ; Hu, Rong-Xiang ; Tang, Lei ; Zhang, Shan-Wen ; Huang, De-Shuang
Author_Institution :
Intell. Comput. Lab., Chinese Acad. of Sci., Hefei, China
Abstract :
In this paper, an efficient feature extraction algorithm called orthogonal linear local spline discriminant embedding (O-LLSDE) is proposed for face recognition. Derived from local spline embedding (LSE), O-LLSDE not only inherits the advantages of LSE which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. Extensive experiments on standard face databases demonstrate the effectiveness of the proposed method.
Keywords :
face recognition; feature extraction; face recognition; feature extraction; local geometry representation; local spline embedding; local tangent space; orthogonal linear local spline discriminant embedding; standard face databases; Equations; Principal component analysis;
Conference_Titel :
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6916-1
DOI :
10.1109/IJCNN.2010.5596905