Title :
Face Recognition Based on LFDA and LS-SVM
Author :
Shen, Qi ; Liu, Ruixiang
Author_Institution :
Sch. of Software Eng., Beijing Univ. of Technol., Beijing, China
Abstract :
Face recognition is one of the most challenging research topics in the field of pattern recognition and computer vision. To efficiently deal with this problem, a novel face recognition algorithm is proposed by the combination of local fisher discriminant analysis (LFDA) and least square version of SVM (LS-SVM). Experimental results on real face databases have demonstrated the better performance of the proposed algorithm.
Keywords :
face recognition; least mean squares methods; principal component analysis; support vector machines; computer vision; face databases; face recognition algorithm; least square-support vector machine; local fisher discriminant analysis; pattern recognition; Face recognition; Facial features; Feature extraction; Independent component analysis; Least squares methods; Linear discriminant analysis; Pattern recognition; Principal component analysis; Support vector machine classification; Support vector machines;
Conference_Titel :
Education Technology and Training, 2009. ETT '09. Second International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-0-7695-3936-2
Electronic_ISBN :
978-1-4244-5527-0
DOI :
10.1109/ETT.2009.43
