Enhanced semi-supervised local Fisher discriminant analysis for face recognition

2012 ◽  
Vol 28 (1) ◽  
pp. 244-253 ◽  
Author(s):  
Hong Huang ◽  
Jianwei Li ◽  
Jiamin Liu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis

UNCORRELATED LOCAL FISHER DISCRIMINANT ANALYSIS FOR FACE RECOGNITION

2011 ◽  
Vol 25 (06) ◽  
pp. 863-887 ◽  
Author(s):  
HONG HUANG ◽  
JIAMIN LIU ◽  
HAILIANG FENG
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Fisher Discriminant Analysis ◽  
Global Optimal Solution ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis ◽  
Global Optimal ◽  
Eigen Decomposition

An improved manifold learning method, called Uncorrelated Local Fisher Discriminant Analysis (ULFDA), for face recognition is proposed. Motivated by the fact that statistically uncorrelated features are desirable for dimension reduction, we propose a new difference-based optimization objective function to seek a feature submanifold such that the within-manifold scatter is minimized, and between-manifold scatter is maximized simultaneously in the embedding space. We impose an appropriate constraint to make the extracted features statistically uncorrelated. The uncorrelated discriminant method has an analytic global optimal solution, and it can be computed based on eigen decomposition. As a result, the proposed algorithm not only derives the optimal and lossless discriminative information, but also guarantees that all extracted features are statistically uncorrelated. Experiments on synthetic data and AT&T, extended YaleB and CMU PIE face databases are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of the proposed method.

Face Recognition Using Tensor Local Fisher Discriminant Analysis

2012 ◽  
Vol 38 (9) ◽  
pp. 1485 ◽  
Author(s):  
Jian-Wei ZHENG ◽  
Wan-Liang WANG ◽  
Xiao-Ming YAO ◽  
Hai-Yan SHI
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis

scholarly journals Face Recognition Using Double Sparse Local Fisher Discriminant Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Zhan Wang ◽  
Qiuqi Ruan ◽  
Gaoyun An
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Optimization Problem ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Locality Preserving ◽  
Local Fisher Discriminant Analysis ◽  
Reduction Methods ◽  
Data Adaptive ◽  
Discriminant Power

Local Fisher discriminant analysis (LFDA) was proposed for dealing with the multimodal problem. It not only combines the idea of locality preserving projections (LPP) for preserving the local structure of the high-dimensional data but also combines the idea of Fisher discriminant analysis (FDA) for obtaining the discriminant power. However, LFDA also suffers from the undersampled problem as well as many dimensionality reduction methods. Meanwhile, the projection matrix is not sparse. In this paper, we propose double sparse local Fisher discriminant analysis (DSLFDA) for face recognition. The proposed method firstly constructs a sparse and data-adaptive graph with nonnegative constraint. Then, DSLFDA reformulates the objective function as a regression-type optimization problem. The undersampled problem is avoided naturally and the sparse solution can be obtained by adding the regression-type problem to al1penalty. Experiments on Yale, ORL, and CMU PIE face databases are implemented to demonstrate the effectiveness of the proposed method.

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