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.

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.

Two-Dimensional Orthogonal Unsupervised Discriminant Projection

2012 ◽  
Vol 542-543 ◽  
pp. 1343-1346
Author(s):  
Xing Zhu Liang ◽  
Yu E Lin ◽  
Jing Zhao Li
Keyword(s):  
Feature Extraction ◽  
Sample Size ◽  
Recognition Performance ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Small Sample ◽  
Two Dimensional ◽  
Small Sample Size Problem ◽  
Size Problem ◽  
Discriminant Projection

Unsupervised Discriminant Projection (UDP) is one of the most promising feature extraction methods. However, UDP suffers from the small sample size problem and the optimal basis vectors obtained by the UDP are nonorthogonal. In this paper, we present a new method called Two-dimensional Orthogonal Unsupervised Discriminant Projection (2DOUDP), which is not necessary to convert the image matrix into high-dimensional image vector and does not suffer the small sample size problem. To further improve the recognition performance, the orthogonal projection matrix obtained based on Gram–Schmidt orthogonalization is given. Experimental results on ORL database indicate that the proposed 2DOUDP method achieves better recognition rate than the UDP method and some other orthogonal feature extraction algorithms.

scholarly journals 3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2128
Author(s):  
Zhenye Li ◽  
Hongyan Zou ◽  
Xinyan Sun ◽  
Tingting Zhu ◽  
Chao Ni
Keyword(s):  
Face Recognition ◽  
Sample Size ◽  
Transfer Learning ◽  
Facial Recognition ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Small Sample ◽  
3D Face Recognition ◽  
3D Face ◽  
Siamese Network

Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a significant improvement in the recognition rate, but small sample sizes represent a new problem. In this paper, we present an expression-invariant 3D face recognition method based on transfer learning and Siamese networks that can resolve the small sample size issue. First, a landmark detection method utilizing the shape index was employed for facial alignment. Then, a convolutional network (CNN) was constructed with transfer learning and trained with the aligned 3D facial data for face recognition, enabling the CNN to recognize faces regardless of facial expressions. Following that, the weighted trained CNN was shared by a Siamese network to build a 3D facial recognition model that can identify faces even with a small sample size. Our experimental results showed that the proposed method reached a recognition rate of 0.977 on the FRGC database, and the network can be used for facial recognition with a single sample.

Direct Orthogonal Marginal Fisher Analysis

2012 ◽  
Vol 468-471 ◽  
pp. 1203-1206
Author(s):  
Yue Lin ◽  
Jing Zhao Li ◽  
Xing Zhu Liang
Keyword(s):  
Sample Size ◽  
High Performance ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Feature Space ◽  
Small Sample ◽  
High Dimensional ◽  
Small Sample Size Problem ◽  
Optimal Basis ◽  
Size Problem

The recently proposed Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problem and the optimal basis vectors obtained by the MFA are nonorthogonal. In this paper, we present a new method called Direct Orthogonal Marginal Fisher Analysis (DOMFA), which is able to extract all the orthogonal discriminant vectors simultaneously in the high-dimensional feature space without pre-processing using PCA and does not suffer the small sample size problem. Experimental results on ORL database indicate that the proposed DOMFA method achieves better recognition rate than the MFA method and some other orthogonal feature extraction algorithms.

Discriminant Analysis for Biometric Recognition

2011 ◽  
pp. 25-29
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Sample Size ◽  
Linear Discriminant Analysis ◽  
Small Sample Size ◽  
Small Sample ◽  
Biometric Recognition ◽  
Linear Discriminant

This chapter is a brief introduction to biometric discriminant analysis technologies — Section I of the book. Section 2.1 describes two kinds of linear discriminant analysis (LDA) approaches: classification-oriented LDA and feature extraction-oriented LDA. Section 2.2 discusses LDA for solving the small sample size (SSS) pattern recognition problems. Section 2.3 shows the organization of Section I.

An Action Classification Based on 2D2MSD

2013 ◽  
Vol 753-755 ◽  
pp. 3064-3067
Author(s):  
Ju Zhong ◽  
Ye Zi Sheng ◽  
Chun Li Lin ◽  
Nai Dong Cui
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Human Action ◽  
Small Sample ◽  
Experimental Results ◽  
Two Dimensional ◽  
Small Sample Size Problem ◽  
Action Classification ◽  
Average Recognition Rate

Double-direction two-dimensional Maximum Scatter Difference (2D2MSD) based on Maximum Scatter Difference (MSD) was proposed,which overcame the small sample size problem of LDA, and data were more concise. In the Weizmann human action database, experimental results showed the algorithm was fast, the average recognition rate reached 92% and the highest recognition rate reached 100%.

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