Recognition and Sex Categorization of Adults' and Children's Faces: Examining Performance in the Absence of Sex-Stereotyped Cues

Wild, Heather A.; Barrett, Susan E.; Spence, Melanie J.; O'Toole, Alice J.; Yi, Chunhui; Brooke, Jessica

doi:10.1006/jecp.1999.2554

Cited by 105 publications

(149 citation statements)

References 37 publications

(34 reference statements)

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“…We decided to use adult in addition to neonate faces to ensure that our experimental paradigm was working correctly. Because we expected sex categorization of the adult faces to be nearly perfect (Wild et al, 2000), the adult categorization task served as our baseline condition. None of the faces were familiar to the participants.…”

Section: Stimuli and Proceduresmentioning

confidence: 99%

“…Answers to this question are rapid and accurate when viewing adult or child faces (Wild et al, 2000) but are only slightly better than chance when judging infant faces (Porter, Cernoch, & Balogh, 1984;Round & Deheragoda, 2002). Why is sex categorization of infant faces so difficult?…”

mentioning

confidence: 99%

“…The plausible reason is that there are very few differences in facial skeletal structure between prepubescent boys and girls but numerous differences in adult faces (see, e.g., Enlow, 1982). Nevertheless, it seems clear from a perceptual standpoint that there is sufficient information in children's (Cheng, O'Toole, & Abdi, 2001;Wild et al, 2000) and neonates' (Porter et al, 1984;Round & Deheragoda, 2002) faces to allow for accurate sex categorization. The apparent contradiction between skeletal and behavioral information suggests that adults may use other perceptual cues instead of facial traits to assess the sex of a face (e.g., holistic information or different spatialfrequency [SF] channels), or that adults' ability to categorize sex in infants' faces has been overestimated.…”

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confidence: 99%

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Is it a he or a she? Behavioral and computational approaches to sex categorization

Kaminski

Méary

Mermillod

et al. 2011

Atten Percept Psychophys

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Can people categorize the sex of neonate faces? Our experiment tested the sex categorization of neonate faces by adult participants. We used a set of 120 Caucasian faces (adults and 4-day-old neonates) that were presented just once to a large sample of participants. A computational model of low-level visual processing, based on Gabor filters, was used to explore the relation between spatial-frequency information and sex categorization. The results showed that participants were able to categorize the sex of the faces, but were less accurate with neonate (d' = 0.36, β = -.97) than with adult (d' = 3.02, β = -.93) faces. Moreover, faces were more frequently categorized as boys' than girls' faces. The computational model suggests that specific spatialfrequency channels carry most of the useful information for the categorization task. Overall, the findings reveal that subtle differences in neonate facial structure were enough to allow the sex categorization of neonate faces, although accuracy was low in both adults and the computational model of low-level visual processing.

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Section: Stimuli and Proceduresmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Is it a he or a she? Behavioral and computational approaches to sex categorization

Kaminski

Méary

Mermillod

et al. 2011

Atten Percept Psychophys

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“…They concluded that humans are unable to attend selectively to either identity or sex information in faces. However, a more direct method for testing the co-dependence of sex and identity information in faces was used by Wild and colleagues [125]. They compared recognition and sex classification of children's and adults' faces.…”

Section: Sexmentioning

confidence: 99%

“…The prototypes indicate that the faces of boys and girls should be physically distinguishable, but that the differences are indeed subtle and difficult to describe. [125].…”

Section: Sexmentioning

confidence: 99%

Predicting Human Performance for Face Recognition

2006

Face Processing

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The ability of humans to recognize faces provides an implicit benchmark for gauging the performance of automatic face recognition algorithms. IntroductionHuman face recognition abilities are impressive by comparison to the performance of many current automatic face recognition systems. Such is the belief of many psychologists and computer scientists. This belief, however, is supported more by anecdotal impression than by scientific evidence. In fact, there are relatively few systematic and direct comparisons between the performance of humans and automatic face recognition algorithms, (though see [84], for exceptions). Comparisons of these sorts can be carried out both at a quantitative and qualitative level. On the qualitative side, as the field of automatic face recognition develops, the assessment of working systems has expanded to consider, not only overall levels of performance, but performance under more restricted and targeted conditions. It may thus be important to know, for example, whether an algorithm performs accurately recognizing "male faces of a certain age", "female faces of a certain ethnicity", or faces that are "typical" in the context

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