IAPR/IEEE Winter School on Biometrics 2023

Face Recognition at a Distance

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In recent years we have witnessed increasingly diverse application scenarios of biometrics systems in our daily life, one of which is face recognition at a distance. In this talk, I will cover a number of key problems that are recently being addressed at the computer vision lab of MSU, including: 1) how to address the various training sample quality in learning large-scale face recognition systems; 2) how to bridge the gap between the training data distribution and test data distribution; and 3) how to integrate identity information from an image set or video sequences? In the end, I will also briefly mention other biometric research efforts in my group, such as reducing biasness, and detect deepfake, adversarial, and spoof samples.


Dr. Xiaoming Liu is a Professor at the Department of Computer Science and Engineering of Michigan State University (MSU). 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 face related analysis. Since 2012, he helps to develop a strong computer vision area in MSU, who is ranked top 15 nationally according to the 5-year statistics at csrankings.org. He received the 2018 Withrow Distinguished Scholar Award from MSU. He has been Area Chairs for numerous conferences, including CVPR, ICCV, ECCV, ICLR, NeurIPS, ICML, the Co-Program Chair of BTAS’18, WACV’18, and AVSS’21 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 28 U.S. patents. His work has been cited over 10,000 times according to Google Scholar, with an H-index of 52. He is a fellow of IAPR.

Xiaoming Liu

Xiaoming Liu
Michigan State University, US