About AMP Lab Projects Downloads Publications People Links
Office: Porter Hall B41
Lab: Porter Hall B6
Department of ECE, Carnegie Mellon University,
5000 Forbes Avenue, Pittsburgh,
[Research Interests] [Project] [Publications]
based face recognition
Although face recognition has been an active research topic for decades, the traditional recognition algorithms are all based on static images. In this project, we would like to propose algorithms for video based face recognition because it has superior advantages over the image-based recognition. Such as, the temporal information of faces can be utilized to facilitate the recognition task; and further, a better modeling technique can be applied to the video sequence of human faces.
updating for pattern recognition
In patter recognition and computer vision, appearance modeling is an important way for object tracking and recognition. While many modeling methods have been proposed, few of them were dealing with the updating of the model, which is very useful because of the lacking training data and always under-going changing of the object's appearance. In this work we propose to update the eigenspace model according the changing statistics of the samples being observed.
With electronic communication starting to dominate large areas of every day life, a safe method of verifying user is essential to prevent data misuse. Because of the appearance variations in faces, the authentication of persons based on human faces is a complex task with high performance and robustness requirements when used in practical applications. We are interested in different ways to deal with the variations in face images, such as expression, registration error, illumination and poses.
Based Human Animation
My earlier work concentrate on the analysis of human motion in realistic scene and its application in human animation. Human animation is a challenging domain in computer animation. To arm at many shortcomings of conventional techniques, we propose a new video based human animation technique. The key idea of this approach is to recover the 3D human motion information from a monocular 2D image sequence.
With the fast development of Internet and digital technology, more and more data are readily available on the web. How to extract the needed information from the deep sea of web has become a critical issue, which is identified as web-based Multimedia Information Retrieval (MIR). The initiative of this research is to explore ways of intelligent MIR. In Zhejiang University, I concentrated my work on the video similarity model, and using semantic template to support image retrieval.
Top of this page
Top of this page
Xiaoming Liu and Tsuhan Chen, Geometry-assisted Statistical Modeling for Face Mosaicing, submitted to ICIP 2003.
Xiaoming Liu, Tsuhan Chen, Video-Based Face Recognition Using Adaptive Hidden Markov Models, submitted to CVPR 2003.
Xiaoming Liu and Tsuhan Chen, Shot Boundary Detection Using Temporal Statistics Modeling, Accepted for publication in the Proceeding of the IEEE International Conference on Acoustics, Speech, and Signal Processing 2002.
Xiaoming Liu, Tsuhan Chen and B.V.K. Vijaya Kumar, On Modeling Variations For Face Authentication, Accepted for publication in the Proceeding of the International Conference on Automatic Face and Gesture Recognition 2002.
Xiaoming Liu, Yueting Zhuang and Yunhe Pan. Video Based Human Animation Technique. In: Proceeding of The 7th ACM International Multimedia Conference (Multimedia 99). Orlando, Florida, USA. October 30-November 5, 1999.
Top of this page