Sex differences in face gender recognition in humans
Introduction
The human face presents a clear sexual dimorphism 7., 10., 11., 18., 19.. Face gender recognition is an extremely efficient and fast cognitive process [6]. Even when images are cropped to remove all cultural cues to gender such as hairstyle and make-up, gender classification is correct in almost 100% of the cases in adult subjects [6], whereas 7-years-old children already reach 80% accuracy in the same task [21]. These data clearly indicate that biological cues in facial anatomy are sufficient for a very efficient gender recognition and this ability is acquired early during childhood.
Male and female faces differ both for shape and texture and both shape and texture cues are used for face gender recognition. In frontal views, texture is more salient than shape for gender classification whereas the salience of shape becomes greater in lateral view 5., 12.. Isolated facial parts can be used for face gender classification and the eye region has the highest load in judging gender, followed by the face outline 3., 16., 22.. Some global aspects of face configuration, however, are also important for face gender classification 16., 17..
The present study was undertaken to answer these two basic questions:
- 1.
Is gender recognition equally efficient for male and female faces?
- 2.
Is there an interaction between gender of the observer and gender of the target face?
Since ceiling effects are prominent in face gender recognition, to address these questions it is necessary to increase the difficulty of the gender classification task. Masking by spatial filtration is a widely-used technique in psychophysics. We have used two different and complementary spatial filtration techniques to mask pictures of male and female faces and study the effect of spatial filtration on gender recognition. In one case, a pixelation filter was used. This type of filtration greatly disturbs shape information but spares information concerning average colour distribution in the image. The second filter we have used is a Gaussian noise filter: it destroys information concerning average colour composition of the image leaving detection of high-contrast edges relatively spared. We found that both modalities of spatial filtration affect recognition of female faces more than recognition of male faces. In addition, male subject were more efficient in recognising male faces whereas female subjects were more efficient in recognising female faces regardless of the spatial filtration method used.
Section snippets
Subjects
One hundred twenty-one healthy observers (56 males, age span 17–63, 30 ± 5 years and 65 females, age span 19–46, 28 ± 5 years) voluntarily participated at Experiment 1. Fifty-five healthy observers (33 males, age span 17–63, 29 ± 6 years and 22 females, age span 20–46, 29 ± 5 years) participated in Experiment 2. Fifty-two healthy observers: (24 males, age span 19–56, 32 ± 5 years and 28 females, age span 19–45, mean 27 ± 5 years) participated in Experiment 3.
The subjects were previously
Experiment 1: effects of pixelation
Face gender recognition of full-resolution pictures is extremely efficient [6]. In order to increase the difficulty of the recognition task we used a pixelation spatial filter to progressively reduce the amount of information available for gender classification (see Section 2). An example of the result of the 32 pixelation filter is shown in Fig. 1. When filtered by the 32 pixelation filter, faces are represented by only 112 pixels. Male (N = 56) and female (N = 65) subjects were presented with
Discussion
Face gender recognition is an extremely efficient cognitive process, nearing 100% correct guesses for frontal unkown pictures [6]. To test the existence of sex differences in face gender processing, we have increased the difficulty of face gender categorisation by using two different modes of spatial filtration: pixelation and Gaussian noise. Our study comes to two main conclusions:
- 1.
Male faces are categorised more efficiently than female faces.
- 2.
Subjects are more efficient in categorising same-sex
Acknowledgements
This work was supported by Scuola Normale Superiore grant SNS-03 and MIUR grant 2003062953.
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