Linear Discriminant Analysis (LDA) is frequently used for dimension reduction and has been successfully utilized in many applications, especially face recognition. In classical LDA, however, the definition of the between-class scatter matrix can cause large overlaps between neighboring classes, because LDA assumes that all classes obey a Gaussian distribution with the same covariance. We therefore, propose an adaptive nonparametric discriminant analysis (ANDA) algorithm that maximizes the distance between neighboring samples belonging to different classes, thus improving the discriminating power of the samples near the classification borders. To evaluate its performance thoroughly, we have compared our ANDA algorithm with traditional PCA+LDA, Orthogonal LDA (OLDA) and nonparametric discriminant analysis (NDA) on the FERET and ORL face databases. Experimental results show that the proposed algorithm outperforms the others.
An Adaptive Nonparametric Discriminant Analysis Method and Its Application to Face Recognition
Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007
2007-01-01
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Linear Discriminant Analysis , Nearest Neighbors , Nonparametric Discriminant Analysis , Face Recognition Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence , Biometrics , Algorithm Analysis and Problem Complexity
SVM-Based Nonparametric Discriminant Analysis, An Application to Face Detection
British Library Conference Proceedings | 2003
|British Library Conference Proceedings | 2001
|Nonparametric subspace analysis for face recognition
IEEE | 2005
|