scholarly journals Inter-subspace distance: a new method for face recognition with multiple samples

2004 ◽  
Author(s):  
Jiun-Hung Chen ◽  
Shih-Liang Yeh ◽  
Chu-Song Chen
Keyword(s):  
Face Recognition ◽  
New Method ◽  
Subspace Distance ◽  
Multiple Samples

A new method of NIR face recognition using kernel projection DCV and neural networks

2013 ◽  
Author(s):  
Ya Qiao ◽  
Yuan Lu ◽  
Yun-song Feng ◽  
Feng Li ◽  
Yongshun Ling
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
New Method

Algorithm Study of Face Recognition on Improved 2DLDA

2011 ◽  
Vol 128-129 ◽  
pp. 58-61
Author(s):  
Shi Ping Li ◽  
Yu Cheng ◽  
Hui Bin Liu ◽  
Lin Mu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Small Sample ◽  
New Method ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Face Representation ◽  
Paired Class ◽  
The Relationship

Linear Discriminant Analysis (LDA) [1] is a well-known method for face recognition in feature extraction and dimension reduction. To solve the “small sample” effect of LDA, Two-Dimensional Linear Discriminant Analysis (2DLDA) [2] has been used for face recognition recently，but its could hardly take use of the relationship between the adjacent scatter matrix. In this paper, I improved the between-class scatter matrix, proposed paired-class scatter matrix for face representation and recognition. In this new method, a paired between-class scatter matrix distance metric is used to measure the distance between random paired between-class scatter matrix. To test this new method, ORL face database is used and the results show that the paired between-class scatter matrix based 2DLDA method （N2DLDA） outperforms the 2DLDA method and achieves higher classification accuracy than the 2DLDA algorithm.

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