Event Location: NSH 1305
Bio: Dr. Peter Tu, Ph.D. Oxford University.Dr. Tu joined GE Global Research
in 1997. Prior to this, he was a member of the Sony Computer Vision
Group in Tokyo, Japan. He has developed a number of algorithms for
latent fingerprint matching that have been incorporated into the FBI
AFIS system. Dr. Tu has also made contributions to GE’s optical
metrology systems, which are used to make high precision 3D shape
measurements on manufactured parts. He has developed a number of
techniques based on Helmholtz imaging which directly addresses issues
associated with specularity and high curvature. Currently, Dr. Tu is
focused on multi-view surveillance with the aim of achieving reliable
behavior recognition in complex environments. He manages the $4 million
intelligent video research effort. He has authored more than 25
publications, has more than 20 U.S. patents pending. Peter has served as
PI for the NIJ “High Quality 3D Facial Images from Surveillance Video”
the FBI “ReFace” program.

Dr. Xiaoming Liu is a research scientist at General Electric (GE) Global
Research. He received the B.E. degree from Beijing Information
Technology Institute, Beijing, China and the M.E. degree from Zhejiang
University, Hangzhou, China, in 1997 and 2000 respectively, both in
Computer Science, and the Ph.D. degree in Electrical and Computer
Engineering from Carnegie Mellon University (CMU), in 2004. His research
interests include computer vision, pattern recognition, and machine
learning, with a recent focus on facial image processing in the context
of surveillance videos. At GE, he is the PI for the current NIJ
“Site-Adaptive Face Recognition at a Distance” program and the project
leader of the BIRD “ID Kiosk” program. He has lead the execution of the
NIJ “Active 3D Face Capture” program and was the main contributor of the
NIJ “High Quality 3D Facial Images from Surveillance Video” program. He
has authored more than 40 peer-reviewed scientific publications, and has
over 10 U.S. patents pending.

Abstract: The first part of the talk will give an overview of the various
intelligence video projects being developed at the Visualization and
Computer Vision Lab of GE Global Research. The second part of the talk
presents a discriminative framework for efficiently aligning images.
Although conventional generative model based Active Appearance Models
(AAM) have achieved some success, they suffer from the generalization
problem, i.e., how to align any image with a generic model. We treat the
iterative image alignment problem as a process of maximizing the score
of a trained two-class classifier that is able to distinguish correct
alignment (positive class) from incorrect alignment (negative class).
During the modeling stage, given a set of images with ground truth
landmarks, we train a conventional Point Distribution Model (PDM) and a
boosting-based classifier, which we call Boosted Appearance Model (BAM).
When tested on an image with the initial landmark locations, the
proposed algorithm iteratively updates the shape parameters of the PDM
via the gradient ascent method such that the classification score of the
warped image is maximized. The proposed framework is applied to the face
alignment problem. Using extensive experimentation, we show that,
compared to the AAM-based approach, this framework greatly improves the
robustness, accuracy and efficiency of face alignment by a large margin,
especially for unseen data. We will also present two recent extensions
to BAM. On the modeling side, we have employed a ranked learning scheme
to ensure that a convex alignment cost surface is obtained. On the
feature representation side, extracting features from the image
observation space enables the fitting process to explicitly take
advantage of edge information. Parts of this talk have been/will be
published in several vision conferences, including BMVC 06,07, CVPR
07,08, and ICCV 2007.