HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Thompson, R. W. Articles by Dagnelie, G. Search for Related Content PubMed PubMed Citation Articles by Thompson, R. W., Jr Articles by Dagnelie, G.

Facial Recognition Using Simulated Prosthetic Pixelized Vision

¹From the Lions Vision Research and Rehabilitation Center, Wilmer Ophthalmological Institute, The Johns Hopkins University School of Medicine, Baltimore, Maryland; and the ²Doheny Retina Institute, Keck School of Medicine, University of Southern California, Los Angeles, California.

PURPOSE. To evaluate a model of simulated pixelized prosthetic vision using noncontiguous circular phosphenes, to test the effects of phosphene and grid parameters on facial recognition.

METHODS. A video headset was used to view a reference set of four faces, followed by a partially averted image of one of those faces viewed through a square pixelizing grid that contained 10 x 10 to 32 x 32 dots separated by gaps. The grid size, dot size, gap width, dot dropout rate, and gray-scale resolution were varied separately about a standard test condition, for a total of 16 conditions. All tests were first performed at 99% contrast and then repeated at 12.5% contrast.

RESULTS. Discrimination speed and performance were influenced by all stimulus parameters. The subjects achieved highly significant facial recognition accuracy for all high-contrast tests except for grids with 70% random dot dropout and two gray levels. In low-contrast tests, significant facial recognition accuracy was achieved for all but the most adverse grid parameters: total grid area less than 17% of the target image, 70% dropout, four or fewer gray levels, and a gap of 40.5 arcmin. For difficult test conditions, a pronounced learning effect was noticed during high-contrast trials, and a more subtle practice effect on timing was evident during subsequent low-contrast trials.

CONCLUSIONS. These findings suggest that reliable face recognition with crude pixelized grids can be learned and may be possible, even with a crude visual prosthesis.

This article has been cited by other articles:

				J. G. Ravin and P. M. Odell Pixels and Painting: Chuck Close and the Fragmented Image Arch Ophthalmol, August 1, 2008; 126(8): 1148 - 1151. [Abstract] [Full Text] [PDF]

				C. de Balthasar, S. Patel, A. Roy, R. Freda, S. Greenwald, A. Horsager, M. Mahadevappa, D. Yanai, M. J. McMahon, M. S. Humayun, et al. Factors Affecting Perceptual Thresholds in Epiretinal Prostheses Invest. Ophthalmol. Vis. Sci., June 1, 2008; 49(6): 2303 - 2314. [Abstract] [Full Text] [PDF]

				G. Dagnelie, D. Barnett, M. S. Humayun, and R. W. Thompson Jr Paragraph text reading using a pixelized prosthetic vision simulator: parameter dependence and task learning in free-viewing conditions. Invest. Ophthalmol. Vis. Sci., March 1, 2006; 47(3): 1241 - 1250. [Abstract] [Full Text] [PDF]

				A. P. Fornos, J. Sommerhalder, B. Rappaz, A. B. Safran, and M. Pelizzone Simulation of Artificial Vision, III: Do the Spatial or Temporal Characteristics of Stimulus Pixelization Really Matter? Invest. Ophthalmol. Vis. Sci., October 1, 2005; 46(10): 3906 - 3912. [Abstract] [Full Text] [PDF]