Facial Recognition Using Simulated Prosthetic Pixelized Vision

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. 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 10x10 to 32x32 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. 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. These findings suggest that reliable face recognition with crude pixelized grids can be learned and may be possible, even with a crude visual prosthesis.