Xiaoming Liu

Xiaoming  Liu
  • Assistant Professor
  • Computer Science and Engineering
  • PhD, Electrical and Computer Engineering, Carnegie Mellon University
  • MS, Computer Science and Engineering, Zhejiang University
  • BA, Computer Science and Engineering, Beijing Information Technology Institute
  • 428 S. Shaw Lane, Room 1126
  • Engineering Building
  • East Lansing, MI 48824
  • 517.355.2359


Computer Vision Laboratory

Personal Website



Xiaoming Liu is one of the world leading researchers on facial analysis.  Over the past decade, he has made a number of important and widely recognized contributions to this area ranging from face recognition, face alignment, to 3D face reconstruction. The central theme of his research is to enhance visual intelligence by designing robust, efficient, and self-aware computer vision algorithms.

In image alignment, Dr. Liu developed a novel discriminative modeling framework - Boosted Appearance Models (BAM). By formulating image alignment as a classification problem, his framework was shown to significantly outperform the conventional generative model in both accuracy and generalization capability. In object recognition, Dr. Liu has developed advanced algorithms based on eigenflow and image mosaicing to cope with intra-class variations. His contributions also include exploiting dynamic information for online and adaptive object recognition. These contributions lead to robust and accurate facial recognition under different conditions.

The impact of his research is reflected in the high citation of his papers by other researchers. According to Google Scholar, Dr. Liu’s papers have been cited more than 4,000 times, with an H-Index of 33.2. His work has significantly advanced the state of the art in image alignment, video-based recognition, and computer vision systems and applications. He has been issued six patents for his work.



  • computer vision
  • pattern recognition
  • machine learning
  • deep learning
  • biometrics
  • human computer interface
  • medical image analysis
  • image and video processing and multimedia retrieval