TY - JOUR
T1 - Facial Recognition Using Simulated Prosthetic Pixelized Vision
AU - Thompson, Robert W.
AU - Barnett, G. David
AU - Humayun, Mark S.
AU - Dagnelie, Gislin
PY - 2003/11
Y1 - 2003/11
N2 - 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.
AB - 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.
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U2 - 10.1167/iovs.03-0341
DO - 10.1167/iovs.03-0341
M3 - Article
C2 - 14578432
AN - SCOPUS:0142200814
SN - 0146-0404
VL - 44
SP - 5035
EP - 5042
JO - Investigative Ophthalmology and Visual Science
JF - Investigative Ophthalmology and Visual Science
IS - 11
ER -