Systems Seminar
3D Human Face Recognition Using Summation Invariants
Prof. Yu Hen Hu
UW ECE Department
Abstract
A novel family of geometrically invariant features, called
summation invariants,
are proposed for the recognition of the 3D surface
of human faces. In particular, a 2D semi-local summation invariant
feature is extracted from each column and each row of a rectangular
region surrounding the nose of a 3D facial depth map. Through
extensive experimentation, we empirically identify the most efficient
2D summation invariant features. We also investigate the proper
pre-processing method for the 2D summation invariant features. Tested
with the 3D facial data from the Face Recognition Grand Challenge v1.0
dataset, the proposed new features exhibit significant performance
improvement over the baseline algorithm distributed with the dataset.
This is joint work with Wei-Yang Lin, Kin-Chung Wong, and Nigel Boston
Time and Place: Wed., Oct. 26, at 3:30 pm in 4610 Engr. Hall.
SYSTEMS SEMINAR WEB PAGE:
http://homepages.cae.wisc.edu/~gubner/seminar/