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Contactless Print Capture |
Keywords: image processing, machine learning, fingerprints, palm prints, hand geometry, contactless capture, friction ridge capture
Citation:
Abstract
The ability to quickly compute hand geometry measurements
from a freely posed hand offers advantages to biometric
identification systems. While hand geometry systems are not
new, typical measurements of lengths and widths of fingers
and palms require rigid placement of the hand against pegs.
Slight deviations in hand position, finger stretch or pressure
can yield different measurements. This paper offers novel
approaches to computing hand geometry measurements from
frontal views of freely posed hands. These approaches offer
advantages in hygiene, comfort and reliability. Our algorithms
segment the hand from a known background under
spot lights and locate feature points along the fingers and
wrists. Given a database of 54 hand images, with three different
images of the same hand of each subject, our approach
uniquely identified a previously unseen hand with an overall
accuracy of 92%.
Xiaoqian Jiang, Wanhong Xu,
Latanya Sweeney,
Yiheng Li, Ralph Gross, and Daniel Yurovsky.
New Directions in Contact Free Hand Recognition.
IEEE International Conference on Image Processing (ICIP). San Antonio, Texas, September 2007.
(PDF).
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