MSU Foundation Professor
Xiaoming Liu works on computer vision, machine learning, and biometrics, especially on face related analysis.
Get in touchXiaoming Liu earned his Ph.D degree in Electrical and Computer Engineering from Carnegie Mellon University in 2004. He received a B.E. degree from Beijing Information Technology Institute, China and a M.E. degree from Zhejiang University, China in 1997 and 2000 respectively, both in Computer Science. Prior to joining MSU, he was a research scientist at the Computer Vision Laboratory of GE Global Research. His research interests include computer vision, pattern recognition, machine learning, biometrics, ... human computer interface, etc.
Read MoreZhejiang University: M.S., Computer Science and Engineering | 2000
Carnegie Mellon University: Ph.D., Electrical and Computer Engineering | 2004
Beijing Information Technology Institute: B.A., Computer Science and Engineering | 1997
MSU Today | 2021-06-16
“Our method will facilitate deepfake detection and tracing in real-world settings where the deepfake image itself is often the only information detectors have to work with,” said Xiaoming Liu, MSU Foundation Professor of computer science. “It’s important to go beyond current methods of image attribution because a deepfake could be created using a generative model that the current detector has not seen during its training.”
CNBC | 2021-06-16
Schick questioned whether Facebook’s tool would work on the latter, adding that “there can never be a one size fits all detector.” But Xiaoming Liu, Facebook’s collaborator at Michigan State, said the work has “been evaluated and validated on both cases of deepfakes.” Liu added that the “performance might be lower” in cases where the manipulation only happens in a very small area.
Fortune | 2021-06-16
Hassner says the research took inspiration from prior work by a Michigan State computer scientist who collaborated on the project, Xiaoming Liu. Liu had studied the subtle differences between images taken with different brands and kinds of digital cameras. He built machine-learning systems that could analyze images and determine, with a high degree of accuracy, the type of camera used to take that particular picture.
Biometric Update | 2020-11-07
Michigan State University biometrics researcher Sixue Gong explains a method for de-biasing facial recognition described in a research paper written with Xiaoming Liu and Anil Jain in an interview with Biometric Update. The idea is one of several promising attempts to move beyond improving training dataset balance to address the problem, which Sixue says is necessary.