Wei-Shi Zheng  (郑伟诗) I have joint the Sun Yat-sen University as a faculty under the one-hundred people program. Please contact me by email. The update of this website will be continued. Go to the same webpage at my department   Now:   Assistant Professor School of Information Science and Technology  Sun Yat-sen University, China E-mail: wszheng@ieee.org Previous: Department of Computer Science, Queen Mary University of London, United Kingdom

Chapter:

1. Wei-Shi Zheng, JianHuang Lai, Pong C. Yuen, "Linear Dimension Reduction," Encyclopedia of Biometrics, Springer, pp. 899-903, 2009.

2. Wei-Shi Zheng, JianHuang Lai, Xiaohua Xie, Yan Liang, Pong C. Yuen, and Yaoxian Zou, "Kernel Methods for Facial Image Preprocessing," Pattern Recognition, Machine Intelligence and Biometrics---Expanding Frontiers, Springer and High Education Press, 2011.

Journal Publications:

1. Wei-Shi Zheng, Shaogang Gong, and Tao Xiang, "Quantifying and Transferring Contextual Information in Object Detection," IEEE Trans. on Pattern Analysis and Machine Intelligence, 2011, accepted, (Published online: 18 August 2011; DOI: 10.1109/TPAMI.2011.164).

2. Wei-Shi Zheng, JianHuang Lai, Shengcai Liao, and Ran He, "Extracting Non-negative Basis Images Using Pixel Dispersion Penalty," accepted by Pattern Recognition, 2012.

3. Wei-Shi Zheng, JianHuang Lai, and Pong C. Yuen, "Penalized Pre-image Learning in Kernel Principal Component Analysis," IEEE Trans. on Neural Networks, vol. 21, no. 4, pp. 551-570, 2010.

4. Wei-Shi Zheng, JianHuang Lai, Pong C. Yuen, and Stan Z. Li, "Perturbation LDA: Learning the Difference between the Class Empirical Mean and Its Expectation," Pattern Recognition, vol. 42, no. 5, pp. 764-779, 2009.

5. Wei-Shi Zheng, JianHuang Lai, and Stan Z. Li, "1D-LDA versus 2D-LDA: When Is Vector-based Linear Discriminant Analysis Better than Matrix-based?" Pattern Recognition, vol. 41, no. 7, pp. 2156-2172, 2008.

6. Wei-Shi Zheng, JianHuang Lai, and Pong C. Yuen, "GA-fisher: A New LDA-based Face Recognition Algorithm with Selection of Principal Components," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 35, no. 5, pp. 1065-1078, 2005.

7. Ran He, Wei-Shi Zheng, and BaoGang Hu, "Maximum Correntropy Criterion for Robust Face Recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1561 - 1576, 2011.

8. Xiaohua Xie, Wei-Shi Zheng, JianHuang Lai, Pong C. Yuen, and Ching Y. Suen, "Normalization of Face Illumination Based on Large- and Small- Scale Features," IEEE Trans. on Image Processing, vol. 20, no. 7, pp. 1807 - 1821, 2011.

9. Ran He, Wei-Shi Zheng, and BaoGang Hu, and Xiang-Wei Kong, "A Regularized Correntropy Framework for Robust Pattern Recognition," Neural Computation, vol. 23, no. 8, pp. 2074-2100, 2011.

10. Guoyun Lian, JianHuang Lai, and Wei-Shi Zheng, "Spatial-temporal Consistent Labeling of Tracked Pedestrians across Non-overlapping Camera Views," Pattern Recognition, vol. 44, no. 5, pp. 1121-1136, 2011.

11. Xiaohua Xie, JianHuang Lai, and Wei-Shi Zheng, "Extraction of Illumination Invariant Facial Features from A Single Image Using Nonsubsampled Contourlet Transform," Pattern Recognition, vol. 43, no.12, pp. 4177–4189, 2010.

12. Ran He, BaoGang Hu, Wei-Shi Zheng, and Xiang-Wei Kong, "Robust Principal Component Analysis Based on Maximum Correntropy Criterion," IEEE Trans. on Image Processing, vol. 20, no. 6, pp. 1485-1494, 2011.

Top Conferences:

1. Wei-Shi Zheng, Shaogang Gong, and Tao Xiang, "Transfer Re-identification: From Person to Set-based Verification", IEEE Conf. on Computer Vision and Pattern Recognition, Rhode Island, USA, June 2012.

2. Wei-Shi Zheng, Shaogang Gong, and Tao Xiang, "Person Re-identification by Probabilistic Relative Distance Comparison", IEEE Conf. on Computer Vision and Pattern Recognition, 2011 (Acceptance rate: 26.4%)

3. Wei-Shi Zheng, Shaogang Gong, and Tao Xiang, "Quantifying Contextual Information for Object Detection," In Proc. IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, October, 2009. (Acceptance rate: 23%)

4. Wei-Shi Zheng, Stan Z. Li, JianHuang Lai, and Shengcai Liao, “On Constrained Sparse Matrix Factorization,” In Proc. IEEE International Conference on Computer Vision (ICCV), Brazil, 2007. (Acceptance rate: 23%)

5. Ran He and Wei-Shi Zheng, "Nonnegative Sparse Coding for Discriminative Semi-supervised Learning," IEEE Conf. on Computer Vision and Pattern Recognition, 2011. (Acceptance rate: 26.4%)

6. Xiaohua Xie, Wei-Shi Zheng, JianHuang Lai, and Pong C. Yuen, "Face Illumination Normalization on Large and Small Scale Features," In Proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2008. (Acceptance rate: 31%)

7. Ran He, BaoGang Hu, Wei-Shi Zheng, and YanQing Guo, "Two-stage Sparse Representation for Robust Recognition on Large-scale Database," In Proc. Twenty-Fourth AAAI Conference on Artificial Intelligence , 2010 (oral). (Acceptance rate: 26.9%)

8. Ran He, Tieniu Tan, Zhenan Sun, and Wei-Shi Zheng, "Recovery of Corrupted Low-Rank Matrices via Half-Quadratic based Nonconvex Minimization," IEEE Conf. on Computer Vision and Pattern Recognition, 2011. (Acceptance rate: 26.4%)

9. Ran He, Tieniu Tan, Liang Wang, and Wei-Shi Zheng, "l_2,1 Regularized Correntropy for Robust Feature Selection", IEEE Conf. on Computer Vision and Pattern Recognition, Rhode Island, USA, June 2012.

Main Conferences in Computer Vision and Machine Learning:

1. Wei-Shi Zheng, Shaogang Gong, and Tao Xiang, "Unsupervised Selective Transfer Learning for Object Recognition," Asian Conference on Computer Vision (ACCV) 2010.

2. Wei-Shi Zheng, Shaogang Gong, and Tao Xiang, "Associating Groups of People," In Proc. British Machine Vision Conference (BMVC), London, September 2009.

Academic Activity

- Reviewer for:
  * IEEE Trans. on Pattern Analysis and Machine Intelligence.
  * IEEE Trans. on Neural Networks.
  * Pattern Recognition.
  * IEEE Trans. on Circuits and Systems for Video Technology.
  * IEEE Signal Processing Letters.
  * Statistics and Computing.
  * Neurocomputing.
  * Signal Processing.
  * Image and Vision Computing.
  * Optical Engineering
  * IEEE/IAPR International Conference on Biometrics (ICB), 2007.
  * IAPR International Conference on Pattern Recognition (ICPR), 2006.

- Area Chair, IEEE Conference Series on Advanced Video and Signal based Surveillance, Beijing, 2012.
- Technical Committee Member, IAPR International Conference on Pattern Recognition (ICPR), 2012.
- Program Committee Member, IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS 2012), Washington DC, USA, 2012.
- Program Committee Member/Reviewer, British Machine Vision Conference (BMVC), Guildford, United Kingdom, 2012.
- Program Committee Member, First International Workshop on Re-Identification (Re-Id 2012), in conjunction with ECCV 2012. (to be finally determined)
- Technical Committee Member, IAPR International Conference on Pattern Recognition (ICPR), 2010.
- Technical Committee Member, The 7th International Conference on Natural Computation(ICNC'11)
  and the 8th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'11), 2011.
- Technical Committee Member, The 6th Chinese Conference on Biometric Recognition (CCBR)，2011.

 

Main Research (snapshot): Object Association & Face Recognition

OBJECT ASSOCIATION
		Context Quantification for Object Detection >> Context is critical for minimising ambiguity in object detection. In this work, a novel context modelling framework is proposed without the need of any prior scene segmentation or context annotation. This is achieved by  exploring a new polar geometric histogram descriptor for context representation. In order to quantify context, we formulate a new context risk function and a maximum margin context (MMC) model to solve the minimization problem of the risk function. Crucially, the usefulness and goodness of contextual information is evaluated directly and explicitly through a discriminant context inference method and a context confidence function, so that only reliable contextual information that is relevant to object detection is utilised. --- W.-S. Zheng, S. Gong, and T. Xiang, "Quantifying Contextual Information for Object Detection," ICCV 2009. [PDF] Wei-Shi Zheng, Shaogang Gong, and Tao Xiang, "Quantifying and Transferring Contextual Information in Object Detection," accepted by IEEE Trans. on Pattern Analysis and Machine Intelligence, 2011.
		Group of People Matching In a crowded public space, people often walk in groups, either with people they know or strangers. Associating a group of people over space and time can assist understanding individual's behaviours as it provides vital visual context for matching individuals within the group. Seemingly an `easier' task compared with person matching, this problem is in fact very challenging because a group of people can be highly non-rigid with changing relative position of people within the group and severe self-occlusions. For the first time, the problem of matching/associating groups of people over large space and time captured in multiple non-overlapping camera views is addressed by us. Specifically, a novel people group representation and a group matching algorithm are proposed. The former addresses changes in the relative positions of people in a group and the latter deals with variations in illumination and viewpoint across camera views. We also demonstrate a notable enhancement on individual Person matching by utilising the group description as visual context. --- W.-S. Zheng, S. Gong, and T. Xiang, "Associating Groups of People," BMVC 2009. [PDF]
		Person Re-identification Developed a probabilistic relative distance comparison model for matching individual person across non-overlapping camera views in a surveillance system. The advantage of this model is that rather than maximizing the difference in distances between a pair of relevant person images and any related irrelevant pair, the proposed model is a soft discriminant model and only directly minimizes the probabilistic error of the relative distance comparison between them, which is proved to be more effective for person re-identification in order to alleviate the over-fitting problem due to the large variations of person’s appearance across non-overlapping camera views --- W.-S. Zheng, S. Gong, and T. Xiang, "Person Re-identification by Probabilistic Relative Distance Comparison", IEEE Conf. on Compuer Vision and Pattern Recognition, 2011. [PDF] * An extended paper has been submitted for peer review.
		Kernel Adaptation Transfer A kernel adaptation transfer technique that utilizes unlabelled auxiliary data to quantify and select the most relevant transferrable knowledge for recognizing a target class object against the background given very limited target training samples. The advantages of this model are that it does not assume that the auxiliary data are labelled nor the relationships between target and auxiliary classes are known a priori, and it performs selective transfer of knowledge extracted from the auxiliary data to minimize the negative knowledge transfer suffered by many existing methods. --- Wei-Shi Zheng, Shaogang Gong, and Tao Xiang, "Unsupervised Selective Transfer Learning for Object Recognition," Asian Conference on Computer Vision (ACCV) 2010.[PDF]
FACE RECOGNITION
	1. Linear Discriminant Feature Extraction (Subspace Methods))
		Principal Component Selection >> There is some argument for principal component selection in PCA+LDA. This work shows small principal components (corresponding to small eigenvalues) are useful and should be carefully selected in PCA+LDA. A foundation of principal component selection in LDA is established. New GA technique is used for implementation. --- Wei-Shi Zheng, J. H. Lai, and Pong C. Yuen, “GA-fisher: A New LDA-based Face Recognition Algorithm with Selection of Principal Components,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 35, no. 5, pp. 1065-1078, 2005. [PDF]
		1D-LDA vs. 2D-LDA >> From 2003 to 2008, lots of work have shown that algorithms with (2D) matrix-based representation perform better than the traditional (1D) vector-based ones. Specially, 2D-LDA was widely reported to outperform 1D-LDA. However, would the matrix-based linear discriminant analysis be always superior and when would 1D-LDA be better? This work gives some impressive theoretical analysis and experimental comparison between 1D-LDA and 2D-LDA. Different from existing views, we find that there is no convinced evidence that 2D-LDA would always outperform 1D-LDA when the number of training samples for each class is small or when the number of discriminant features used is small. --- Wei-Shi Zheng, J. H. Lai, and Stan Z. Li, “1D-LDA versus 2D-LDA: When Is Vector-based Linear Discriminant Analysis Better than Matrix-based?” Pattern Recognition, vol. 41, no. 7, pp. 2156-2172, 2008. [PDF]
		Perturbation LDA >> In deriving the Fisher’s LDA formulation, there is an assumption that the class empirical mean is equal to its expectation. However, this may not be valid in practice and this problem has been rarely discussed before. From the "perturbation" perspective, we develop a new algorithm, called perturbation LDA (P-LDA), in which perturbation random vectors are introduced to learn the effect of the difference between the class empirical mean and its expectation in Fisher criterion. --- Wei-Shi Zheng, J. H. Lai, Pong C. Yuen, and Stan Z. Li, "Perturbation LDA: Learning the Difference between the Class Empirical Mean and Its Expectation," Patten Recognition, vol. 42, no. 5,  pp. 764-779, 2009. [PDF] [Code]
	2. Sparse Feature Learning for Classification
		Sparse One-sided Non-negative Matrix Factorization >> NMF, which is a two-sided non-negativity based matrix factorization, is popular for extraction of sparse features. However, why non-negativity should be imposed on both components and coefficients? What is case if some constraint is released? In this work, we find releasing the non-negativity constraint on the coefficient term in NMF would help extract equally/much sparser and more reconstrutive components/features as compared to the two-sided non-negativity matrix factorization techniques. The exact 17 local components of Swimmer data set are successfully extracted for the first time (to our best knowledge). --- Wei-Shi Zheng, Stan Z. Li, J. H. Lai, and Shengcai Liao, “On Constrained Sparse Matrix Factorization,” 11th IEEE International Conference on Computer Vision (ICCV), 2007. [PDF] Wei-Shi Zheng, JianHuang Lai, Shengcai Liao, and Ran He, "Extracting Non-negative Basis Images Using Pixel Dispersion Penalty," Pattern Recognition, vol. 45, no. 8, pp. 2912-2926, 2012.[PDF][CODE]
		Sparse Representation based Classifier We present a sparse correntropy framework for computing robust sparse representations of face images for recognition. Compared with the state-of-the-art l1norm-based sparse representation classifier (SRC), which assumes that noise also has a sparse representation, our sparse algorithm is developed based on the maximum correntropy criterion, which is much more insensitive to outliers. In the proposed correntropy frameworks, several new methods have been developed for face recognition and object recognition. --- Ran He, Wei-Shi Zheng, and BaoGang Hu, "Maximum Correntropy Criterion for Robust Face Recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1561 - 1576, 2011. Ran He, Wei-Shi Zheng, and BaoGang Hu, and Xiang-Wei Kong, "A Regularized Correntropy Framework for Robust Pattern Recognition," Neural Computation, vol. 23, no. 8, pp. 2074-2100, 2011.
	3. Kernel Methods for Nonlinear Processing
		Pre-image Learning >> KPCA is a promising technique for nonlinear processing of images. A main problem in this approach is how to learn the pre-image of a kernel feature point in the input image space. However, it is always ill-posed. We present a regularized method and introduce the weakly supervised learning in order to alleviate this ill-posed estimation problem. --- Wei-Shi Zheng, J. H. Lai, “Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis,” 18th International Conference on Pattern Recognition (ICPR), 2006. (top 15%, oral) [PDF] Wei-Shi Zheng, J. H. Lai, and Pong C. Yuen, “Weakly Supervised Learning on Pre-image Problem in Kernel Methods,” 18th International Conference on Pattern Recognition (ICPR), 2006. [PDF] Wei-Shi Zheng, J. H. Lai, and Pong C. Yuen, "Penalized Pre-image Learning in Kernel Principal Component Analysis," IEEE Trans. on Neural Networks, 2010. [PDF] [Code]
	4. Image Factorization for Face Image Processing
		Illumination Normalization >> In solving the illumination problem for face recognition, most (if not all) existing methods either only use extracted small-scale features while discard large-scale features, or perform normalization on the whole image. In the latter case, small-scale features may be distorted when the large-scale features are modified. In this work, we argue that large-scale features of face image are important and contain useful information for face recognition as well as visual quality of normalized image. We suggest that illumination normalization should mainly perform on large-scale features of face image rather than the whole face image. An new framework is therefore developed. --- Xiaohua Xie, Wei-Shi Zheng, J. H. Lai, and Pong C. Yuen, “Face Illumination Normalization on Large and Small Scale Features,” CVPR 2008. [PDF] Xiaohua Xie, Wei-Shi Zheng, JianHuang Lai, Pong C. Yuen, and Ching Y. Suen, "Normalization of Face Illumination Based on Large- and Small- Scale Features," IEEE Trans. on Image Processing, vol. 20, no. 7, pp. 1807 - 1821, 2011.

 

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