SEMINAR

Person Recognition at a Distance

Speaker

Dr. Xiaoming Liu

Working

Michigan State University

Timeline

Mon, Dec 9 2024 - 10:00 am (GMT + 7)

About Speaker

Dr. Xiaoming Liu is the MSU Foundation Professor, and Anil and Nandita Jain Endowed Professor at the Department of Computer Science and Engineering of Michigan State University (MSU). He is also a visiting 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 face related analysis and 3D vision. 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 is an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He has authored more than 200 scientific publications, and has filed 35 U.S. patents. His work has been cited over 28000 times according to Google Scholar, with an H-index of 79. He received the 2018 and 2023 Withrow Distinguished Scholar Awards from MSU. He is a fellow of The Institute of Electrical and Electronics Engineers (IEEE) and International Association for Pattern Recognition (IAPR).

Abstract

In recent years we have witnessed increasingly diverse application scenarios of biometrics systems in our daily life, one of which is person 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: how to address the various training sample quality in learning large-scale face recognition systems; how to integrate identity information from an image set or video sequences; how to estimate the 3D body shape from an image of clothed human body; how to use AIGC to generate a complete synthetic database for training face recognition systems; how to leverage foundation models for person recognition; and how to build our own foundation models for unified face and body recognition.