By Topic

Facial Recognition From Volume-Rendered Magnetic Resonance Imaging Data

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

7 Author(s)
Fred W. Prior ; Sch. of Med., Washington Univ., St. Louis, MO ; Barry Brunsden ; Charles Hildebolt ; Tracy S. Nolan
more authors

Three-dimensional (3-D) reconstructions of computed tomography (CT) and magnetic resonance (MR) brain imaging studies are a routine component of both clinical practice and clinical and translational research. A side effect of such reconstructions is the creation of a potentially recognizable face. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule requires that individually identifiable health information may not be used for research unless identifiers that may be associated with the health information including ldquoFull face photographic images and other comparable imagesrdquo are removed (de-identification). Thus, a key question is: Are reconstructed facial images comparable to full-face photographs for the purpose of identification? To address this question, MR images were selected from existing research repositories and subjects were asked to pair an MR reconstruction with one of 40 photographs. The chance probability that an observer could match a photograph with its 3-D MR image was 1 in 40 (0.025), and we considered 4 successes out of 40 (4/40, 0.1) to indicate that a subject could identify persons' faces from their 3-D MR images. Forty percent of the subjects were able to successfully match photographs with MR images with success rates higher than the null hypothesis success rate. The Blyth-Still-Casella 95% confidence interval for the 40% success rate was 29%-52%, and the 40% success rate was significantly higher (P< 0.001) than our null hypothesis success rate of 1 in 10 (0.10).

Published in:

IEEE Transactions on Information Technology in Biomedicine  (Volume:13 ,  Issue: 1 )