Research Interests

Computer Vision, Multimodal AI

Education

  • Sep 2017-Jul 2021 University of Chinese Academy of Sciences Ph.D.
    Computer Application Technology Advisors:Prof. Xiangdong Zhou and Prof. Yong Feng
  • Sep 2009-Jan 2012 Tianjin University M.S.
    Computer Science and Technology Advisors:Prof. Wenjun Wang
  • Sep 20005-Jul 2009 Tianjin University B.A.
    Computer Science and Technology

Work experience

  • Jan 2022-Present, Chinese Academy of Sciences, Institute of Automation, Visiting Scholar
  • Aug 2021-Present, University of South China, School of Computer Science, Teaching&Research
  • Aug 2013-Jul 2017, University of South China, Information Center, Information Management
  • Jul 2012-Jul 2013, Alibaba Inc.,B2B,Software Development

Publications

  • Jiang F, Li Q, Wang W, et al. Open-Set Single-Domain Generalization for Robust Face Anti-Spoofing[J]. International Journal of Computer Vision, 2024: 1-22.
  • Fangling Jiang, Yunfan Liu, Haolin Si, Jingjing Meng, Qi Li. Cross-scenario Unknown-Aware Face Anti-spoofing with Evidential Semantic Consistency Learning[J]. IEEE Transactions on Information Forensics and Security,2024.
  • Jiang, Fangling, Qi Li, Pengcheng Liu, Xiang-Dong Zhou, and Zhenan Sun. Adversarial learning domain-invariant conditional features for robust face anti-spoofing[J]. International Journal of Computer Vision, 2023, 131: 1680-1703.
  • Fangling Jiang,Pengcheng Liu,Xiang-Dong Zhou. Ordinal regression with representative feature strengthening for face anti-spoofing[J]. Neural Computing and Applications, 2022, 34(18):15963-15979.
  • Fangling Jiang,Pengcheng Liu,Xiangdong Zhou. A review on face anti-spoofing[J]. Acta Automatica Sinica,2021, 47(8): 1799-1821.
  • Fangling Jiang,Pengcheng Liu, Xiaohu Shao, et al. Face anti-spoofing with generated near-infrared images[J]. Multimedia Tools and Applications,2020: 1-25.
  • Fangling Jiang,Pengcheng Liu,Xiangdong Zhou. Multilevel fusing paired visible light and near-infrared spectral images for face anti-spoofing[J]. Pattern Recognition Letters,2019,128: 30-37.

Teaching

  • Machine Learning
  • Software Project Management
  • Unified Modeling Language