• 深圳市政府资助，医学大数据库收集与医学图像分析，2018/6/1-2019/12/31

• 国家自然科学基金, 61671427, 弱监督视觉目标检测, 2017/1/1—2020/12/31

• University of Oulu, Finland, Heart ratio measurement from VIS lighting conditions, 2015/3/1-2015/4/31

• 国家自然科学基金, 61271433, 多视角多姿态人体目标检测, 2013/1/1-2016/12/31

• University of Oulu, Finland, Local descriptor for face recognition, 2012/9/1-2012/11/31

• Academy of Finland, Affective human-robot interaction, 01/2009 - 12/2018

• Finland Tekes, Joint Research in Face Analysis and Visual Surveillance (JointFavis), 04/2008 - 03/2010

• Academy of Finland, Texture analysis in machine vision, 09/2007- 12/2018

授课

• 深度学习及其应用， University of Oulu, Finland, 2018.

• 深度学习及其应用， University of Oulu, Finland, 2017.

• 深度学习及其应用， University of Oulu, Finland, 2015.

• 计算机图形学，University of Oulu, Finland, 2010-2016 （助教，部分内容教学）

学术荣誉

• 中国第一届生物测定学竞赛（BVC2004）人脸验证竞赛第一名

• IAPR ICB 2006 人脸验证竞赛第一名，该竞赛由英国University of Surrey的Josef Kittler 组织。

• 2005年国家科技进步二等奖，获奖项目：人脸识别理论、技术、系统及其应用

• 2015年国家科技进步二等奖，获奖项目：视觉模式的局部建模及非线性特征获取理论与方法研究

（a）目标检测与分割

1.J.Chen, S. Shan, C. He, G. Zhao, M. Pietikäinen, X. Chen, and W. Gao. WLD: A Robust Local Image Descriptor.IEEE Trans. on Pattern Analysis and Machine Intelligence. 32(9):1705-1720, 2010 (SCI: 9.5)（引用排名在AI领域从2010到2014的五年内的所有文献中排名第56，数据是基于WoS Core的统计）（TPAMI）（国际顶级期刊）（引用846次）

2.J. Chen, G. Zhao, M. Salo, E. Rahtu, and M. Pietikäinen, Automatic Dynamic Texture Segmentation Using Local Descriptors and Optical Flow,IEEE Trans. on Image Processing, 2013 (58citations by Google Scholar) (SCI: 5.1)（TIP）（国际顶级期刊）

3.J.Chen, X. Chen, J. Yang, S. Shan, R. Wang, and W. Gao, Optimization of a training set for more robust face detection,Pattern recognition,41(11):2828-2840, 2009 (29citations by Google Scholar) (SCI: 4.0) (PR)（国际顶级期刊）

4.J. Chen, R.Wang, S. Yan, S. Shan, X. Chen, and W. Gao. Enhancing Human Face Detection by Resampling Examples through Manifolds.IEEE Trans. on System Man, and Cybernetics.37(6):1017-1028, 2007.11 (39citations by Google Scholar) (SCI: 5.1) (TSMC)（国际顶级期刊）

5.J.Chen, S. Shan, G. Zhao, X. Chen, W. Gao, and M. Pietikäinen. A Robust Descriptor based on Weber's Law.IEEE International Conference on Computer Vision and Pattern Recognition,CVPR 2008 (62citations by Google Scholar) (CVPR)（国际顶级会议）

6. Q. Liu, X. Hong, B. Zou,J. Chen, Z. Chen, Hierarchical Contour Closure based Holistic Salient Object Detection,IEEE Transactions on Image Processing, 2017 (SCI: 5.1) (TIP)（国际顶级期刊）

7. Y. Xu, X. Hong,J. Chen, X. Liu, F. Porikli, G. Zhao, Saliency Integration: An Arbitrator Model,IEEE Transactions on Multimedia,2019 (SCI: 4.0)（TMM）（国际顶级期刊）

8. S. Yan, S. Shan, X. Chen, W. Gao, andJ.Chen. Matrix-Structural Learning (MSL) of Cascaded Classifier from Enormous Training Set.IEEE International Conference on Computer Vision and Pattern Recognition,2007 (40 citations by Google Scholar) (CVPR)（国际顶级会议）

9. Q. Ye, T. Zhang, Q. Qiu, B. Zhang,J. Chen, and G. Sapiro, Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model,IEEE International Conference on Computer Vision and Pattern Recognition,2017 (CVPR)（国际顶级会议）

（b）对近期最具有代表性的特征与基于深度卷积网络的特征表达方法进行了详尽的性能评估，并对过去20年内特征表示方法进行了全面综述

1. L. Liu,J. Chen, P. Fieguth, G. Zhao, R. Chellappa, M. Pietikainen, From BoW to CNN: Two Decades of Texture Representation for Texture Classification,International Journal of Computer Vision2019 (IJCV) (SCI: 11.5)

（c）基于深度卷积神经网络（Convolutional Neural Network, CNN）的特征学习与表达

1. L. Liu,J. Chen, G. Zhao, P. Fieguth, X. Chen, M. Pietikäinen, Texture Classification in Extreme Scale Variations using GANet,IEEE Trans. Image Processing, (Accepted)

2. W. Ke,J.Chen, J. Jiao, G. Zhao, Q. Ye, SRN: Side-output Residual Network for Object Symmetry Detection in the Wild,IEEE International Conference on Computer Vision and Pattern Recognition,2017 (Oral, 1.72%录用率) (CVPR)（国际顶级会议）

3. X. Zhang, L. Liu, Y. Xie,J. Chen, L. Wu, M. Pietikäinen, Rotation Invariant Local Binary Convolution Neural Networks, International Conference on Computer Vision Workshop, 2017.

（d）模式识别

1.J.Chen, D. Yi, J. Yang, G. Zhao, S. Li, and M. Pietikäinen, Learning Mappings for Face Synthesis from Near Infrared to Visual Light Images,IEEE International Conference on Computer Vision and Pattern Recognition,2009 (78citations by Google Scholar,Google统计计算机视觉&模式识别领域影响力最高的刊物) (CVPR)（国际顶级期刊）

2.J. Chen, V. Kellokumpu, G. Zhao, M. Pietikäinen, RLBP: robust local binary pattern,British machine vision conference, 2013 (69citations by Google Scholar) (BMVC)（国际顶级会议）

3. R. Wang, S. Shan, X. Chen,J. Chen, and W. Gao. Maximal Linear Embedding for Dimensionality Reduction.IEEE Trans. on Pattern Analysis and Machine Intelligence. 33(9):1776-1792, 2011 (63citations by Google Scholar) (SCI: 9.5)（TPAMI）（国际顶级期刊）

4. S. Xie, S. Shan, X. Chen, andJ. Chen, Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition,IEEE Trans. on Image Processing, 19(5), pp: 1349-1361, 2010, (373citations by Google Scholar) (SCI: 5.1) （引用排名在AI领域从2010到2014五年内的所有文献中排名第225，数据是基于WoS Core的统计）(TIP)（国际顶级期刊）

（e）计算机视觉/模式识别算法在医学领域中的应用

1. X. Qi, G. Zhao,J. Chen, M. Pietikäinen, Exploring Illumination Robust Descriptors for Human Epithelial Type 2 Cell Classification,Pattern Recognition, 2016 (SCI: 4.0) (PR)（国际顶级期刊）

2. X. Li,J.Chen, G. Zhao and M. Pietikäinen, Remote heart rate measurement from face videos under realistic situations.IEEE International Conference on Computer Vision and Pattern Recognition, 2014. (171citations by Google Scholar) (CVPR)（国际顶级会议）

3.J. Chen, X. Qi, O. Tervonen, O. Silven, G. Zhao, and M. Pietikäinen, “Thorax disease diagnosis using deep convolutional neural network,” in EMBC, 2016, pp. 2287–2290.

（f）国际顶级刊物专刊

1. M. Pietikainen, L. Liu,J. Chen, X. Wang, G. Zhao, R. Chellappa, Compact and Efficient Feature Representation and Learning in Computer Vision, Editorial for a special issue onIEEE Trans. on Pattern Analysis and Machine Intelligence, 2018 (SCI: 9.5)（TPAMI）

2. M. Pietikäinen,L. Liu,J. Qin,J. Chen,W. Ouyang,L. V. Gool, Efficient Visual Recognition, Editorial for a special Issue ofInternational Journal of Computer Vision, 2019 (accepted) (SCI: 11.5，人工智能领域影响因子最高的期刊)（IJCV）

对计划招收的硕士和博士研究生的基本要求：(点击此处查看招生信息)

1. 专业范围：计算机科学技术、电子工程、通信、自动控制、应用数学、物理、医学等专业本硕毕业生。

2. 研究/开发能力：具有合作精神，探索能力和创新精神，愿意按较高标准严格要求自己。