scholarly journals Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yue Liu ◽  
Yibing Li ◽  
Hong Xie ◽  
Dandan Liu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Facial Features ◽  
Fisher Criterion ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Face Images ◽  
Kernel Fisher Discriminant Analysis ◽  
Multiple Data ◽  
Novel Algorithm

Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm.

scholarly journals An Empirical Study of Greedy Kernel Fisher Discriminants

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Tom Diethe
Keyword(s):  
Discriminant Analysis ◽  
Matching Pursuit ◽  
Support Vector ◽  
Relative Importance ◽  
Fisher Criterion ◽  
Fisher Discriminant Analysis ◽  
Original Algorithm ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis ◽  
Vector Machines

A sparse version of Kernel Fisher Discriminant Analysis using an approach based on Matching Pursuit (MPKFDA) has been shown to be competitive with Kernel Fisher Discriminant Analysis and the Support Vector Machines on publicly available datasets, with additional experiments showing that MPKFDA on average outperforms these algorithms in extremely high dimensional settings. In (nearly) all cases, the resulting classifier was sparser than the Support Vector Machine. Natural questions that arise are what is the relative importance of the use of the Fisher criterion for selecting bases and the deflation step? Can we speed the algorithm up without degrading performance? Here we analyse the algorithm in more detail, providing alternatives to the optimisation criterion and the deflation procedure of the algorithm, and also propose a stagewise version. We demonstrate empirically that these alternatives can provide considerable improvements in the computational complexity, whilst maintaining the performance of the original algorithm (and in some cases improving it).

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