Advanced Multimedia Processing Lab -- Group Member -- Xiaoming

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Group Member

Xiaoming Liu
PhD. Student

Personal Homepage: http://www.andrew.cmu.edu/~xiaoming
Email: xiaoming@andrew.cmu.edu

Office: Porter Hall B41
Lab: Porter Hall B6
Phone: 412-268-7100
Fax: 412-268-3890
Mailing Address:
Department of ECE, Carnegie Mellon University,
5000 Forbes Avenue, Pittsburgh,
PA 15213-3890

[Research Interests]        [Project]      [Publications

Research Interests

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Project

Video Based Face Recognition

Video based face recognition is studied in this project. For making use of the temporal information, we propose to apply temporal and adaptive Hidden Markov Model (HMM) on face sequence for recognition. For having better modeling of different variations of human faces, a face mosaic model is obtained from training face sequences and used for recognition.

For more details on the project please see the Video based face recognition project page.

 

In patter recognition and computer vision, appearance modeling is an important way for object tracking and recognition. Considering the appearance of the object is always changing, to learn the changing and build an adaptive model are very critical for many applications. In this project, we propose to update the eigenspace model in order to adaptively learn the changing appearance of an object, and use it in different applications.

For more details on the project please see the Model updating project page.

 

Eigenflow Based Face Authentication

Currently the computer only uses password to authenticate the user, we hope  computer can intelligently implement this task by the biometrics approach, i.e. let the face, the voice and the fingerprint of the user do authentication.  The face authentication is a challenging research topic since the human face can always generate significant variations in appearance because of facial expressions, rotation, scale, shift, lighting condition, etc. Here we propose a new approach to perform face authentication tolerant to facial expression variations and registration error.

For more details on the project please see the Face authentication project page.

 

Video Based Human Animation 

In this project, when given a clip of video, we can track human joint in image sequence, then construct corresponding three-dimension human motion skeleton sequence under the perspective projection by use of the camera calibration and human anatomy knowledge, and finally establish a motion library by annotating multiform motion attributes automatically, which can be browsed and queried by the animator. This approach has the characteristic of rich source material, low computing cost, efficient production, and realistic animation result. We demonstrate this on several video clips of people doing full body movements, and visualize the results in re-animating a 3D human skeleton model.

 

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Publications

 

          Conferences Papers:

          Technical Reports: 

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