DocumentCode
1849622
Title
Orthogonal Regularized Linear Discriminant Analysis for face recognition
Author
Feng Li
Author_Institution
Dept. of Comput. Sci. & Technol., Huaqiao Univ., Xiamen, China
Volume
2
fYear
2012
fDate
21-25 Oct. 2012
Firstpage
1213
Lastpage
1216
Abstract
In the paper the Orthogonal Regularized Linear Discriminant Analysis (ORLDA) for face recognition is proposed, which introduces the orthogonal idea to improve regularized linear discriminant analysis. The algorithm not only overcomes the singularity problem but also improves greatly the classified performance under little training samples. The effectiveness of our proposed algorithm is illustrated by Yale, YaleB, UMIST and AR face database.
Keywords
face recognition; visual databases; AR face database; ORLDA; UMIST; Yale; YaleB; face recognition; orthogonal regularized linear discriminant analysis; singularity problem; dimensionality reduction; face recognition; orthogonal vector; regularized linear discriminant analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing (ICSP), 2012 IEEE 11th International Conference on
Conference_Location
Beijing
ISSN
2164-5221
Print_ISBN
978-1-4673-2196-9
Type
conf
DOI
10.1109/ICoSP.2012.6491794
Filename
6491794
Link To Document