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Wen-Sheng Chen - IEEE Xplore Author Profile
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Wen-Sheng Chen

Also published under: Wen Sheng Chen

Publications
11
Citations
162
Publications by Year
20012016
Co-Authors:
Show All Co-Authors (18)
Image of author Wen-Sheng Chen

Wen-Sheng Chen

Also published under: Wen Sheng Chen

Affiliation

College of Mathematics and Statistics, Shenzhen University, China

Publication Topics

Biography

Wen-Sheng Chen received the B.Sc. and Ph.D. degrees in mathematics from Sun Yat-Sen (Zhongshan) University, Guangzhou, China, in 1989 and 1998, respectively.,He is currently an Associate Professor with the Department of Applied Mathematics, Shenzhen University, Shenzhen, China. His current research interests include human face recognition and wavelet analysis and its applications.,Dr. Chen is a member of Chinese Mathematical Society.(Based on document published on 18 July 2005).
Publications
11
Citations
162
Publications by Year
20012016
Co-Authors:
Show All Co-Authors (18)
Author's Published Works

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Simultaneous Dual-Views Reconstruction with Adaptive Dictionary and Low-Rank Representation

Shuangyan Yi;Zhenyu He;Yi Li;Yiu-Ming Cheung;Wen-Sheng Chen

2016 23rd International Conference on Pattern Recognition (ICPR)
Year: 2016 | Conference Paper |
Low-Rank Representation (LRR) is an effective self-expressiveness method, which uses the observed data itself as the dictionary to reconstruct the original data. LRR focuses on representing the global low-dimensional information, but ignores the real fact that data often resides on low-dimensional manifolds embedded in a high-dimensional data. Therefore, LRR can not capture the non-linear geometri...Show More

Simultaneous Dual-Views Reconstruction with Adaptive Dictionary and Low-Rank Representation

Shuangyan Yi;Zhenyu He;Yi Li;Yiu-Ming Cheung;Wen-Sheng Chen

Year: 2016 | Conference Paper |

A novel random projection model for Linear Discriminant Analysis based face recognition

Hui Liu;Wen-Sheng Chen

2009 International Conference on Wavelet Analysis and Pattern Recognition
Year: 2009 | Conference Paper |
Cited by: Papers (3)
Linear discriminant analysis (LDA) is one of the commonly used statistical methods for feature extraction in face recognition tasks. However, LDA often suffers from the small sample size (3S) problem, which occurs when the total number of training data is smaller than the dimension of input feature space. To deal with 3S problem, this paper proposes a novel approach for LDA-based face recognition ...Show More

A novel random projection model for Linear Discriminant Analysis based face recognition

Hui Liu;Wen-Sheng Chen

Year: 2009 | Conference Paper |

Interpolatory Mercer kernel construction for kernel direct LDA on face recognition

Wen-Sheng Chen;Pong C. Yuen

2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2009 | Conference Paper |
Cited by: Papers (3)
This paper proposes a novel methodology on Mercer kernel construction using interpolatory strategy. Based on a given symmetric and positive semi-definite matrix (Gram matrix) and Cholesky decomposition, it first constructs a nonlinear mapping Φ, which is well-defined on the training data. This mapping is then extended to the whole input feature space by utilizing Lagrange interpolatory basis funct...Show More

Interpolatory Mercer kernel construction for kernel direct LDA on face recognition

Wen-Sheng Chen;Pong C. Yuen

Year: 2009 | Conference Paper |

A novel constraint non-negative matrix factorization criterion based incremental learning in face recognition

Wen-Sheng Chen;Bin-Bin Pan;Bin Fang;Jin Zou

2008 International Conference on Wavelet Analysis and Pattern Recognition
Year: 2008 | Conference Paper |
Cited by: Papers (2)
This paper addresses incremental learning and time-consuming problems in non-negative matrix factorization (NMF) of face recognition. When the training samples or classes are incremental, almost all existing NMF based methods must implement repetitive learning. Also, they are usually very time-consuming. To overcome these limitations, we proposed a novel constraint block NMF (CBNMF) method, which ...Show More

A novel constraint non-negative matrix factorization criterion based incremental learning in face recognition

Wen-Sheng Chen;Bin-Bin Pan;Bin Fang;Jin Zou

Year: 2008 | Volume: 1 | Conference Paper |

Wavelet Kernel Construction for Kernel Discriminant Analysis on Face Recognition

Wen-Sheng Chen;Pong Chi Yuen;Jian Huang;Jianghuang Lai

2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06)
Year: 2006 | Conference Paper |
Cited by: Papers (4)
Kernel Discriminant Analysis (KDA) has been shown to be one of the promising approaches to handle the pose and illumination problem in face recognition. However, empirical results show that the performance for KDA on face recognition is sensitive to the kernel function and its parameters. Instead of following existing KDA methods in selecting popular kernel function, this paper proposes a new appr...Show More

Wavelet Kernel Construction for Kernel Discriminant Analysis on Face Recognition

Wen-Sheng Chen;Pong Chi Yuen;Jian Huang;Jianghuang Lai

Year: 2006 | Conference Paper |

Face classification based on Shannon wavelet kernel and modified Fisher criterion

Wen-Sheng Chen;Pong Chi Yuen;Jian Huang;Jianhuang Lai

7th International Conference on Automatic Face and Gesture Recognition (FGR06)
Year: 2006 | Conference Paper |
Cited by: Papers (10)
This paper addresses nonlinear feature extraction and small sample size (S3) problems in face recognition. In sample feature space, the distribution of face images is nonlinear because of complex variations in pose, illumination and face expression. The performance of classical linear method, such as Fisher discriminant analysis (FDA), will degrade. To overcome pose and illumination problems, Shan...Show More

Face classification based on Shannon wavelet kernel and modified Fisher criterion

Wen-Sheng Chen;Pong Chi Yuen;Jian Huang;Jianhuang Lai

Year: 2006 | Conference Paper |

Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition

Wen-Sheng Chen;P.C. Yuen;Jian Huang;Dao-Qing Dai

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Year: 2005 | Volume: 35, Issue: 4 | Journal Article |
Cited by: Papers (92)
This paper addresses two problems in linear discriminant analysis (LDA) of face recognition. The first one is the problem of recognition of human faces under pose and illumination variations. It is well known that the distribution of face images with different pose, illumination, and face expression is complex and nonlinear. The traditional linear methods, such as LDA, will not give a satisfactory...Show More

Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition

Wen-Sheng Chen;P.C. Yuen;Jian Huang;Dao-Qing Dai

Year: 2005 | Volume: 35, Issue: 4 | Journal Article |

A novel subspace LDA algorithm for recognition of face images with illumination and pose variations

Jian Huang;P.C. Yuen;Wen-Sheng Chen

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)
Year: 2004 | Conference Paper |
Cited by: Papers (4)
This paper addresses two LDA problems in face recognition. The first one is small sample size (S3) problem while the second is illumination and pose variations. To overcome the S3 problem, this paper proposes a new method in subspace approach in determining the optimal projection for LDA. Also, an in-depth investigation is conducted on the influence of different illuminations and poses variations....Show More

A novel subspace LDA algorithm for recognition of face images with illumination and pose variations

Jian Huang;P.C. Yuen;Wen-Sheng Chen

Year: 2004 | Volume: 6 | Conference Paper |

Kernel subspace LDA with optimized kernel parameters on face recognition

Jian Huang;P.C. Yuen;Wen-Sheng Chen;J.H. Lai

Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.
Year: 2004 | Conference Paper |
Cited by: Papers (18)
This work addresses the problem of selection of kernel parameters in kernel fisher discriminant for face recognition. We propose a new criterion and derive a new formation in optimizing the parameters in RBF kernel based on the gradient descent algorithm. The proposed formulation is further integrated into a subspace LDA algorithm and a new face recognition algorithm is developed. FERET database i...Show More

Kernel subspace LDA with optimized kernel parameters on face recognition

Jian Huang;P.C. Yuen;Wen-Sheng Chen;J.H. Lai

Year: 2004 | Conference Paper |

Component-based LDA method for face recognition with one training sample

Jian Huang;P.C. Yuen;Wen-Sheng Chen;J.H. Lai

2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443)
Year: 2003 | Conference Paper |
Many face recognition algorithms/systems have been developed in the last decade and excellent performances are also reported when there is sufficient number of representative training samples. In many real-life applications, only one training sample is available. Under this situation, the performance of existing algorithms will be degraded dramatically or the formulation is incorrect, which in tur...Show More

Component-based LDA method for face recognition with one training sample

Jian Huang;P.C. Yuen;Wen-Sheng Chen;J.H. Lai

Year: 2003 | Conference Paper |

Robust facial feature point detection under nonlinear illuminations

Jian Huang Lai;P.C. Yuen;Wen Sheng Chen;S. Lao;M. Kawade

Proceedings IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Year: 2001 | Conference Paper |
Cited by: Papers (11)
Addresses the problem of facial feature point detection under different lighting conditions. Our goal is to develop an efficient detection algorithm, which is suitable for practical applications. The problems that we need to overcome include (1) high detection accuracy, (2) low computational time and (3) nonlinear illumination. An algorithm is developed and reported in the paper. One of the key fa...Show More

Robust facial feature point detection under nonlinear illuminations

Jian Huang Lai;P.C. Yuen;Wen Sheng Chen;S. Lao;M. Kawade

Year: 2001 | Conference Paper |

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