Elsevier

Brain Research Bulletin

Volume 63, Issue 6, 15 July 2004, Pages 443-449
Brain Research Bulletin

Sex differences in face gender recognition in humans

https://doi.org/10.1016/j.brainresbull.2004.03.010Get rights and content

Abstract

Human faces are ecologically-salient stimuli. Face sex is particularly relevant for human interactions and face gender recognition is an extremely efficient cognitive process that is acquired early during childhood. To measure the minimum information required for correct gender classification, we have used a pixelation filter and reduced frontal pictures (28,672 pixels) of male and female faces to 7168, 1792, 448 and 112 pixels. We then addressed the following questions:

Is gender recognition of male and female faces equally efficient?

Are male and female subjects equally efficient at recognising face gender?

We found a striking difference in categorisation of male and female faces. Categorisation of female faces reduced to 1792 pixels is at chance level whereas categorisation of male faces is above chance even for 112 pixel images. In addition, the same difference in the efficiency of categorisation of male and female faces was detected using a Gaussian noise filter.

A clear sex difference in the efficiency of face gender categorisation was detected as well. Female subject were more efficient in recognising female faces. These results indicate that recognition of male and female faces are different cognitive processes and that in general females are more efficient in this cognitive task.

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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