scholarly journals Improvement on Null Space LDA for Face Recognition: A Symmetry Consideration

2005 ◽  
pp. 78-84 ◽  
Author(s):  
Wangmeng Zuo ◽  
Kuanquan Wang ◽  
David Zhang
Keyword(s):  
Face Recognition ◽  
Null Space ◽  
Symmetry Consideration

Kernel Null Space Marginal Fisher Analysis for Face Recognition

2014 ◽  
Vol 889-890 ◽  
pp. 1065-1068
Author(s):  
Yu’e Lin ◽  
Xing Zhu Liang ◽  
Hua Ping Zhou
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Sample Size ◽  
High Performance ◽  
Null Space ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Original Data ◽  
Small Sample ◽  
Neighborhood Structure

In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention. Among them, the Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problems and is still a linear technique. This paper develops a new nonlinear feature extraction algorithm, called Kernel Null Space Marginal Fisher Analysis (KNSMFA). KNSMFA based on a new optimization criterion is presented, which means that all the discriminant vectors can be calculated in the null space of the within-class scatter. KNSMFA not only exploits the nonlinear features but also overcomes the small sample size problems. Experimental results on ORL database indicate that the proposed method achieves higher recognition rate than the MFA method and some existing kernel feature extraction algorithms.

Bagging null space locality preserving discriminant classifiers for face recognition

2009 ◽  
Vol 42 (9) ◽  
pp. 1853-1858 ◽  
Author(s):  
Liping Yang ◽  
Weiguo Gong ◽  
Xiaohua Gu ◽  
Weihong Li ◽  
Yanfei Liu
Keyword(s):  
Face Recognition ◽  
Null Space ◽  
Locality Preserving

Null space discriminant locality preserving projections for face recognition

2008 ◽  
Vol 71 (16-18) ◽  
pp. 3644-3649 ◽  
Author(s):  
Liping Yang ◽  
Weiguo Gong ◽  
Xiaohua Gu ◽  
Weihong Li ◽  
Yixiong Liang
Keyword(s):  
Face Recognition ◽  
Null Space ◽  
Locality Preserving Projections ◽  
Locality Preserving

FUSING GLOBAL AND LOCAL COMPLETE LINEAR DISCRIMINANT FEATURES BY FUZZY INTEGRAL FOR FACE RECOGNITION

2008 ◽  
Vol 22 (07) ◽  
pp. 1427-1445 ◽  
Author(s):  
CHENGYUAN ZHANG ◽  
QIUQI RUAN ◽  
YI JIN
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Null Space ◽  
Information Aggregation ◽  
Recognition Algorithm ◽  
Fuzzy Integral ◽  
Fisher Discriminant Analysis ◽  
Linear Discriminant ◽  
Global And Local

Face recognition becomes very difficult in a complex environment, and the combination of multiple classifiers is a good solution to this problem. A novel face recognition algorithm GLCFDA-FI is proposed in this paper, which fuses the complementary information extracted by complete linear discriminant analysis from the global and local features of a face to improve the performance. The Choquet fuzzy integral is used as the fusing tool due to its suitable properties for information aggregation. Experiments are carried out on the CAS-PEAL-R1 database, the Harvard database and the FERET database to demonstrate the effectiveness of the proposed method. Results also indicate that the proposed method GLCFDA-FI outperforms five other commonly used algorithms — namely, Fisherfaces, null space-based linear discriminant analysis (NLDA), cascaded-LDA, kernel-Fisher discriminant analysis (KFDA), and null-space based KFDA (NKFDA).

WAVELET-FACE BASED SUBSPACE LDA METHOD TO SOLVE SMALL SAMPLE SIZE PROBLEM IN FACE RECOGNITION

2009 ◽  
Vol 07 (02) ◽  
pp. 199-214 ◽  
Author(s):  
WEN-SHENG CHEN ◽  
JIAN HUANG ◽  
JIN ZOU ◽  
BIN FANG
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Dimensionality Reduction ◽  
Sample Size ◽  
Null Space ◽  
Small Sample Size ◽  
Small Sample ◽  
Projection Matrix ◽  
Scatter Matrix ◽  
Linear Discriminant

Linear Discriminant Analysis (LDA) is a popular statistical method for both feature extraction and dimensionality reduction in face recognition. The major drawback of LDA is the so-called small sample size (3S) problem. This problem always occurs when the total number of training samples is smaller than the dimension of feature space. Under this situation, the within-class scatter matrix Sw becomes singular and LDA approach cannot be implemented directly. To overcome the 3S problem, this paper proposes a novel wavelet-face based subspace LDA algorithm. Wavelet-face feature extraction and dimensionality reduction are based on two-level D4-filter wavelet transform and discarding the null space of total class scatter matrix St. It is shown that our obtained projection matrix satisfies the uncorrelated constraint conditions. Hence in the sense of statistical uncorrelation, this projection matrix is optimal. The proposed method for face recognition has been evaluated with two public available databases, namely ORL and FERET databases. Comparing with existing LDA-based methods to solve the 3S problem, our method gives the best performance.

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