Paper Info
Title
Recognition of digital images of the human face at ultra low resolution via illumination spaces
Abstract
Recent work has established that digital images of a human face, collected under various illumination conditions, contain discriminatory information that can be used in classification. In this paper we demonstrate that sufficient discriminatory information persists at ultralow resolution to enable a computer to recognize specific human faces in settings beyond human capabilities. For instance, we utilized the Haar wavelet to modify a collection of images to emulate pictures from a 25- pixel camera. From these modified images, a low-resolution illumination space was constructed for each individual in the CMU-PIE database. Each illumination space was then interpreted as a point on a Grassmann manifold. Classification that exploited the geometry on this manifold yielded error-free classification rates for this data set. This suggests the general utility of a low-resolution illumination camera for set-based image recognition problems.
Year
Venue
Keywords
2007
ACCV
ultra low resolution,human face,low-resolution illumination space,digital image,grassmann manifold,specific human face,human capability,illumination space,error-free classification rate,discriminatory information,low-resolution illumination camera,various illumination condition,low resolution
Field
DocType
Volume
Facial recognition system,Computer vision,Pattern recognition,Computer science,Digital image,Artificial intelligence,Pixel,Discrete wavelet transform,Grassmannian,Haar wavelet,Manifold
Conference
4844
ISSN
ISBN
Citations 
0302-9743
3-540-76389-9
11
PageRank 
References 
Authors
0.75
12
6
Name
Order
Citations
PageRank
Jen-Mei Chang1423.26
Michael Kirby213714.40
Holger Kley3301.46
Chris Peterson46810.93
Bruce Draper5295.12
J. Ross Beveridge61716190.52