Purchase this article with an account.
Robert W. Thompson, G. David Barnett, Mark S. Humayun, Gislin Dagnelie; Facial Recognition Using Simulated Prosthetic Pixelized Vision. Invest. Ophthalmol. Vis. Sci. 2003;44(11):5035-5042. doi: https://doi.org/10.1167/iovs.03-0341.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
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 × 10 to 32 × 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 PDF is available to Subscribers Only