September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Improving face recognition in age-related macular degeneration via caricaturing
Author Affiliations & Notes
  • Jo Lane
    Research School of Psychology, Australian National University, Canberra, Australian Capital Territory, Australia
    Australian Research Council Centre of Excellence for Cognition & Its Disorders, Australian National University, Canberra, Australian Capital Territory, Australia
  • Nick Barnes
    National Information and Communication Technology Australia (NICTA), Canberra, Australian Capital Territory, Australia
    College of Engineering & Computer Science, Australian National University, Canberra, Australian Capital Territory, Australia
  • Xuming He
    National Information and Communication Technology Australia (NICTA), Canberra, Australian Capital Territory, Australia
    College of Engineering & Computer Science, Australian National University, Canberra, Australian Capital Territory, Australia
  • Rohan W Essex
    Australian National University, The Canberra Hospital, and Academic Unit of Ophthalmology, Canberra, Australian Capital Territory, Australia
    John Curtin School of Medical Research (JCSMR), Australian National University, Canberra, Australian Capital Territory, Australia
  • Ted Maddess
    John Curtin School of Medical Research (JCSMR), Australian National University, Canberra, Australian Capital Territory, Australia
  • Emilie Rohan
    John Curtin School of Medical Research (JCSMR), Australian National University, Canberra, Australian Capital Territory, Australia
  • Tamara Gradden
    Research School of Psychology, Australian National University, Canberra, Australian Capital Territory, Australia
  • Jan Provis
    John Curtin School of Medical Research (JCSMR), Australian National University, Canberra, Australian Capital Territory, Australia
    Medical School, Australian National University, Canberra, Australian Capital Territory, Australia
  • Elinor McKone
    Research School of Psychology, Australian National University, Canberra, Australian Capital Territory, Australia
    Australian Research Council Centre of Excellence for Cognition & Its Disorders, Australian National University, Canberra, Australian Capital Territory, Australia
  • Footnotes
    Commercial Relationships   Jo Lane, None; Nick Barnes, None; Xuming He, None; Rohan Essex, None; Ted Maddess, EyeCo Pty Ltd (I), nuCoria Pty Ltd (F); Emilie Rohan, None; Tamara Gradden, None; Jan Provis, Scientific Advisory Board, Eyeco (C); Elinor McKone, None
  • Footnotes
    Support  1. Australian Research Council CE110001021 2. Australian Research Council DP150100684 3. Australian Government as represented by Department of Broadband, Communications, and the Digital Economy; ARC Information and Communication Technologies Centre of Excellence Program 4. ARC Special Research Initiative in Bionic Vision Science and Technology grant to Bionic Vision Australia 5. National Health and Medical Research Council APP1063458
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 23. doi:
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      Jo Lane, Nick Barnes, Xuming He, Rohan W Essex, Ted Maddess, Emilie Rohan, Tamara Gradden, Jan Provis, Elinor McKone; Improving face recognition in age-related macular degeneration via caricaturing. Invest. Ophthalmol. Vis. Sci. 2016;57(12):23.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Patients with age-related macular degeneration (AMD) have difficulty recognising faces and facial expressions. We examined if this could be improved using an image enhancement procedure derived from high-level cortical coding of faces in a perceptual 'face-space', namely caricaturing. Caricaturing exaggerates the ways in which the shape information in an individual face differs from the average. We tested whether caricaturing would improve face identity perception in AMD patients, and facial expression recognition in a simulation of AMD (normal-sighted young adults shown blurred faces).

Methods : To test identity perception, 12 Caucasian AMD patients (mean age 81, range 67-92, 8 females) with mild through severe stages of AMD performed a rating task using monocular vision. Using four levels of caricaturing (0, 20, 40 and 60% exaggeration), and 26 young adult Caucasian faces, participants rated how different two people's faces appeared when compared in pairs. To test expression recognition, 45 Caucasian normal-sighted undergraduates (mean age 22, 36 females) labelled expressions (as happy, sad, anger, fear, disgust, surprise) using two blur levels (50 and 70) to mimic the appearance of different severities of AMD and four levels of caricaturing (0, 40, 80, 100% exaggeration).

Results : For identity, a total of 19 eyes were included in AMD patients with visual acuities (VA) ranging from 6/6 to 6/360. Analysing individual eyes, a significant caricature advantage (at p<.05) was seen in 9/9 (100%) eyes with mild AMD (6/6 to 6/15), 3/6 (50%) eyes with moderate AMD (6/24 to 6/30), and 2/4 (50%) eyes with severe AMD (6/75 and 6/360). No change with caricaturing was found for one patient (both eyes; VA 6/19 and 6/24) and in 3 individual eyes (6/60, 6/120 and 6/360). For expression, caricaturing significantly improved expression recognition (at p<.01) at both blur levels (simulating approximately moderate and severe AMD) with accuracy improved by approximately 7% (e.g., for severe blur, 44% correct in expression labelling without caricaturing, 51% with 100% exaggeration).

Conclusions : Caricaturing can significantly improve perceived differences in facial identity in patients with mild AMD and some patients with moderate and severe AMD. It also significantly improves expression recognition in simulated AMD conditions with normal-sighted young adults, suggesting it should also be useful for expression recognition in patients.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

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