Fengxi Song

Fengxi Song graduated in applied mathematics from the Anhui University (1984). He earned his MSc in applied mathematics from the Changsha Institute of Technology (1987) and PhD in pattern recognition from the Nanjing University of Science and Technology (2004). From 2005 to 2007, he was a postdoctoral fellow at ShenZhen Graduate School, Harbin Institute of Technology. Currently, he is a professor at New Star Research Institute of Applied Technology in Hefei City, (China). He is the author of more than 30 scientific papers in pattern recognition and computer vision. He is a reviewer of several international journals such as IEEE Transactions and Pattern Recognition. His research interests include pattern recognition, computer vision, image processing, and text categorization.

Publications

Advanced Pattern Recognition Technologies with Applications to Biometrics
Advanced Pattern Recognition Technologies with Applications to Biometrics
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 384 pages.
With the increasing concerns on security breaches and transaction fraud, highly reliable and convenient personal verification and identification technologies are more and more...
Overview
Overview
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 23 pages.
A biometric system can be regarded as a pattern recognition system. In this chapter, we discuss two advanced pattern recognition technologies for biometric recognition, biometric...
Discriminant Analysis for Biometric Recognition
Discriminant Analysis for Biometric Recognition
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 5 pages.
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...
Discriminant Criteria for Pattern Classification
Discriminant Criteria for Pattern Classification
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 28 pages.
As mentioned in Chapter II, there are two kinds of LDA approaches: classification- oriented LDA and feature extraction-oriented LDA. In most chapters of this session of the book...
Orthogonal Discriminant Analysis Methods
Orthogonal Discriminant Analysis Methods
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 20 pages.
In this chapter, we first give a brief introduction to Fisher linear discriminant, Foley- Sammon discriminant, orthogonal component discriminant, and application strategies for...
Parameterized Discriminant Analysis Methods
Parameterized Discriminant Analysis Methods
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 28 pages.
In this chapter, we mainly present three kinds of weighted LDA methods. In Sections 5.1, 5.2 and 5.3, we respectively present parameterized direct linear discriminant analysis...
Two Novel Facial Feature Extraction Methods
Two Novel Facial Feature Extraction Methods
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 28 pages.
In this chapter, we introduce two novel facial feature extraction methods. The first is multiple maximum scatter difference (MMSD) which is an extension of a binary linear...
Tensor Space
Tensor Space
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 15 pages.
In this chapter, we first give the background materials for developing tensor discrimination technologies in Section 7.1. Section 7.2 introduces some basic notations in tensor...
Tensor Principal Component Analysis
Tensor Principal Component Analysis
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 22 pages.
Tensor principal component analysis (PCA) is an effective method for data reconstruction and recognition. In this chapter, some variants of classical PCA are introduced and the...
Tensor Linear Discriminant Analysis
Tensor Linear Discriminant Analysis
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 30 pages.
Linear discriminant analysis is a very effective and important method for feature extraction. In general, image matrices are often transformed into vectors prior to feature...
Tensor Independent Component Analysis and Tensor Non-Negative Factorization
Tensor Independent Component Analysis and Tensor Non-Negative Factorization
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 24 pages.
In this chapter, we describe two tensor-based subspace analysis approaches (tensor ICA and tensor NMF) that can be used in many fields like face recognition and other biometric...
Other Tensor Analysis and Further Direction
Other Tensor Analysis and Further Direction
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 27 pages.
In this chapter, we describe tensor-based classifiers, tensor canonical correlation analysis and tensor partial least squares, which can be used in biometrics. Section 11.1 gives...
From Single Biometrics to Multi-Biometrics
From Single Biometrics to Multi-Biometrics
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 19 pages.
In the past decades while biometrics attracts increasing attention of researchers, people also have found that the biometric system using a single biometric trait may not satisfy...
Feature Level Fusion
Feature Level Fusion
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 32 pages.
This chapter introduces the basis of feature level fusion and presents two feature level fusion examples. As the beginning, Section 13.1 provides an introduction to feature level...
Matching Score Level Fusion
Matching Score Level Fusion
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 23 pages.
With this chapter we aims at describing several basic aspects of matching score level fusion. Section 14.1 provides a description of basic characteristics of matching score...
Decision Level Fusion
Decision Level Fusion
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 21 pages.
With this chapter, we first present a variety of decision level fusion rules and classifier selection approaches, and then show a case study of face recognition based on decision...
Book Summary
Book Summary
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang. © 2009. 10 pages.
With the title “Advanced Pattern Recognition Technologies with Applications to Biometrics” this book mainly focuses on two kinds of advanced biometric recognition technologies...