[intermediate] Deep Learning for Trustworthy Biometrics
In recent years we have witnessed increasingly diverse application scenarios of Biometrics in our daily life, despite the societal concerns on some of the weakness of the technology. A sustainable deployment and prospects of biometric systems will rely heavily on the ability to trust the recognition process and its output. As a result, in addition to the high recognition accuracy, trustworthy biometrics has become an emerging research area, with topics ranging from biometrics security (e.g., presentation attack detection and forgery detection), biasness in biometrics, adversarial robustness, to interpretable biometrics. In this course, we will present some of the recent works on these topics and discuss the remaining issues warranting future research.
- Deep learning for presentation attack detection (anti-spoofing)
- Deepfake or image manipulation detection
- Adversarial attack detection
- Mitigate biasness in face recognition
- Interpretable face recognition
Yaojie Liu, Joel Stehouwer, Xiaoming Liu, “On Disentangling Spoof Traces for Generic Face Anti-Spoofing,” in Proceeding of European Conference on Computer Vision (ECCV) 2020, Glasgow, UK, August 2020.
Yaojie Liu, Joel Stehouwer, Amin Jourabloo, Xiaoming Liu, “Deep Tree Learning for Zero-shot Face Anti-Spoofing,” in Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2019), Long beach, CA, June 2019.
Yaojie Liu, Amin Jourabloo, Xiaoming Liu, “Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision,” in Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 2018.
Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, Anil Jain, “On the Detection of Digital Face Manipulation,”in Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, June 2020.
Debayan Deb, Xiaoming Liu, Anil Jain, “FaceGuard: A Self-Supervised Defense Against Adversarial Face Images,” arXiv preprint, Apr. 2021.
Chang Chen, Zhiwei Xiong, Xiaoming Liu, Feng Wu, “Camera Trace Erasing,” in Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, June 2020.
Sixue Gong, Xiaoming Liu, Anil Jain, “Mitigating Face Recognition Bias via Group Adaptive Classifier,” in Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2021), Nashville, TN, June 2021.
Sixue Gong, Xiaoming Liu, Anil Jain, “Jointly De-biasing Face Recognition and Demographic Attribute Estimation,” in Proceeding of European Conference on Computer Vision (ECCV) 2020, Glasgow, UK, August 2020.
Bangjie Yin, Luan Tran, Haoxiang Li, Xiaohui Shen, Xiaoming Liu, “Towards Interpretable Face Recognition,”in Proceeding of International Conference on Computer Vision (ICCV 2019), Seoul, South Korean, Oct. 2019.
Basic Machine Learning and Computer Vision knowledge.
Dr. Xiaoming Liu is the MSU Foundation Professor at the Department of Computer Science and Engineering of Michigan State University (MSU) and also a visiting research scientist at Google Research. He received Ph.D. degree from Carnegie Mellon University in 2004. Before joining MSU in 2012 he was a research scientist at General Electric (GE) Global Research. He works on computer vision, machine learning, and biometrics especially on 3D vision, and face related analysis. Since 2012 he helps to develop a strong computer vision area in MSU who is ranked top 15 in US according to the 5-year statistics at csrankings.org. He received the 2018 Withrow Distinguished Scholar Award from MSU. He has been Area Chair for numerous conferences, including CVPR, ICCV, ECCV, ICLR, NeurIPS, ICML, the Co-Program Chair of BTAS’18, WACV’18, and AVSS’22 conferences, and Co-General Chair of FG’23 conference. He is an Associate Editor of Pattern Recognition Letters, Pattern Recognition, and IEEE Transaction on Image Processing. He has authored more than 150 scientific publications, and has filed 29 U.S. patents. His work has been cited over 15000 times according to Google Scholar, with an H-index of 60. He is a fellow of International Association for Pattern Recognition (IAPR). His research has been widely reported in prominent national and international news outlets including the Wall Street Journal, CNBC, CNET, Engadget, Fortune, the Mac Observer, New Scientist, Silicon Angle, VentureBeat, and the Verge. More information of Dr. Liu’s research can be found at http://cvlab.cse.msu.edu