Caricaturing as a general method to improve poor face recognition: Evidence from low-resolution images, other-race faces, and older adults.

Abstract

There are multiple well-established situations in which humans' face recognition performance is poor, including for low-resolution images, other-race faces, and in older adult observers. Here we show that caricaturing faces-that is, exaggerating their appearance away from an average face-can provide a useful applied method for improving face recognition across all these circumstances. We employ a face-name learning task offering a number of methodological advantages (e.g., valid comparison of the size of the caricature improvement across conditions differing in overall accuracy). Across six experiments, we (a) extend previous evidence that caricaturing can improve recognition of low-resolution (blurred) faces; (b) show for the first time that caricaturing improves recognition and perception of other-race faces; and (c) show for the first time that caricaturing improves recognition in observers across the whole adult life span (testing older adults, M age ϭ 71 years). In size, caricature benefits were at least as large where natural face recognition is poor (other-race, low resolution, older adults) as for the naturally best situation (own-race high-resolution faces in young adults). We discuss potential for practical applicability to improving face recognition in low-vision patients (age-related macular degeneration, bionic eye), security settings (police, passport control), eyewitness testimony, and prosopagnosia. Public Significance StatementAccurate face recognition is important to everyday social interactions and in security settings such as police and passport control. Yet face recognition ability is often quite poor, particularly for other-race faces, for older adults, and for patients with impaired vision who see faces in low resolution. This study demonstrates a method for improving face recognition in all these circumstances: caricaturing the face to exaggerate its image away from the average.

Caricaturing can improve facial expression recognition in low-resolution images and age-related macular degeneration

“…All these signals involve only small physical variations in faces, implying they are likely to be poorly perceived by patients with low-resolution vision, including AMD patients. Caricaturing offers hope of improving recognition of these types of subtle information given that both our present findings and our previous studies of simulated low vision (Dawel et al, 2019;Irons et al, 2014) show that caricaturing tends to be effective particularly when performance is initially impaired, at least as long as accuracy does not become so poor it hits floor.…”

“…In a previous study, we have shown that caricaturing improves AMD patients' ability to perceive facial identity-that is, how one person's face differs from another's-across a wide range of vision loss severities, including for patients with mild, moderate, and even severe vision loss (legally blind; Lane, Rohan, Sabeti, Essex, Maddess, Barnes, et al, 2018). The identity caricaturing benefits in AMD patients also paralleled those in a partial simulation of AMD that used normal vision observers shown face images with added blur (Dawel et al, 2019;Irons et al, 2014;McKone, Robbins, He, & Barnes, 2018).…”

“…We applied uniform spatial blur across the image by reducing the contrast of spatial frequencies higher than a given threshold (with threshold set lower for higher blur levels, using a Gaussian kernel filter of size defined by the cutoff frequency; Supplement S2 provides details). The labels of our two conditions (''Blur 50'' and ''Blur 70'') are somewhat arbitrary, but use numbering consistent with that we have previously employed in face identity studies (in which we tested up to ''Blur30''; Dawel et al, 2019;Irons et al, 2014;McKone et al, 2018): Specifically, the amount of blur is as the Marmor and Marmor formula gives for peripheral viewing of a face assumed to subtend 18.118 along the horizontal (equivalent to a real person viewed at 54 cm) at 508 eccentricity (''Blur 50'' condition) or 708 eccentricity (''Blur 70'' condition). Note that the same amount of blur results in the Marmor and Marmor model for a smaller face seen less far into the periphery.…”

Impacts of impaired face perception on social interactions and quality of life in age-related macular degeneration: A qualitative study and new community resources

“…We thus also asked about image enhancement , which has the potential to improve perception of all aspects of faces. We explained that, in image enhancement, patients would view faces on computers, tablets, or smart glasses [41,42] with the faces digitally altered in ways that have been reported to improve low-resolution face perception (e.g., making the face larger [15,43]; increasing the contrast of medium and high spatial frequencies [44,45]; or caricaturing the face, i.e., exaggerating its appearance away from the average to make identification easier [46–48]). Participants were on the whole very positive about trying image enhancement technology if and when it became available, particularly on TV and/or computer screens (90% support; e.g., one patient mentioned this would be useful when skyping his family), and via smart glasses (90% support).…”

A stimulus set of people famous to current generation Australian undergraduates, with recognition norms for face images and names

“…Famous faces have been used in studies of processing differences for familiar versus unfamiliar faces (Johnston & Edmonds, 2009) or familiar faces varying in social importance to self (famous faces vs. friends' faces, Keyes & Zalicks, 2016). Famous faces can also been used for linking face recognition ability to personality traits such as extraversion (Lander & Poyarekar, 2015), understanding perceptual face coding via image manipulations (e.g., caricaturing, Lee, Byatt, & Rhodes, 2000; Itz, Schweinberger, & Kaufmann, 2016), investigating manipulations to improve poor face recognition (e.g., in the bionic eye, Irons et al, 2017; or for other‐race faces, Dawel et al, 2019), dissociating physical face versus identity representations in the brain (Rotshtein, Henson, Treves, Driver, & Dolan, 2005), and investigating associative memory in parahippocampal cortex (Bar, Aminoff, & Ishai, 2008). Also of relevance in young adults, famous face tests can be used as part of general neuropsychological evaluation (e.g., for amnesia following brain injury, Rizzo, Venneri, & Papagno, 2002), and are required for diagnosing prosopagnosia, both acquired and developmental (e.g., in Australia the MACCS Famous Face Test 2008, Supp to Palermo et al, 2011).…”