By Topic

Facenet: Tracking People and Acquiring Canonical Face Images in a Wireless Camera Sensor Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kyle Heath ; Stanford University, Department of Electrical Engineering, Stanford, CA 94305 ; Leonidas Guibas

We describe a method for tracking people in 2D world coordinates and acquiring canonical frontal face images that fits the sensor network paradigm. Frontal face images are particularly desireable features for tracking and identity management because they are largely invariant to day-to-day changes in appearance. This approach has been implemented and evaluated on a prototype wired camera network called FaceNet. Our primary contribution is to show how sensing the trajectories of moving objects can be exploited to acquire high quality canonical views while conserving node energy. We present an evaluation of the approach and demonstrate the tasking algorithm in action on data acquired from the FaceNet camera network.

Published in:

2007 First ACM/IEEE International Conference on Distributed Smart Cameras

Date of Conference:

25-28 Sept. 2007