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Jessica Irons, Elinor McKone, Rachael Dumbleton, Nick Barnes, Xuming He, Jan Provis, Callin Ivanovici, Alisa Kwa; A new theoretical approach to improving face recognition in disorders of central vision: Face caricaturing. Journal of Vision 2014;14(2):12. doi: https://doi.org/10.1167/14.2.12.
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© ARVO (1962-2015); The Authors (2016-present)
Damage to central vision, of which age-related macular degeneration (AMD) is the most common cause, leaves patients with only blurred peripheral vision. Previous approaches to improving face recognition in AMD have employed image manipulations designed to enhance early-stage visual processing (e.g., magnification, increased HSF contrast). Here, we argue that further improvement may be possible by targeting known properties of mid- and/or high-level face processing. We enhance identity-related shape information in the face by caricaturing each individual away from an average face. We simulate early- through late-stage AMD-blur by filtering spatial frequencies to mimic the amount of blurring perceived at approximately 10° through 30° into the periphery (assuming a face seen premagnified on a tablet computer). We report caricature advantages for all blur levels, for face viewpoints from front view to semiprofile, and in tasks involving perceiving differences in facial identity between pairs of people, remembering previously learned faces, and rejecting new faces as unknown. Results provide a proof of concept that caricaturing may assist in improving face recognition in AMD and other disorders of central vision.
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