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Research Article  |   February 2009
Uncovering gender discrimination cues in a realistic setting
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Journal of Vision February 2009, Vol.9, 10. doi:10.1167/9.2.10
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      Nicolas Dupuis-Roy, Isabelle Fortin, Daniel Fiset, Frédéric Gosselin; Uncovering gender discrimination cues in a realistic setting. Journal of Vision 2009;9(2):10. doi: 10.1167/9.2.10.

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Abstract

Which face cues do we use for gender discrimination? Few studies have tried to answer this question and the few that have tried typically used only a small set of grayscale stimuli, often distorted and presented a large number of times. Here, we reassessed the importance of facial cues for gender discrimination in a more realistic setting. We applied Bubbles—a technique that minimizes bias toward specific facial features and does not necessitate the distortion of stimuli—to a set of 300 color photographs of Caucasian faces, each presented only once to 30 participants. Results show that the region of the eyes and the eyebrows—probably in the light-dark channel—is the most important facial cue for accurate gender discrimination; and that the mouth region is driving fast correct responses (but not fast incorrect responses)—the gender discrimination information in the mouth region is concentrated in the red-green color channel. Together, these results suggest that, when color is informative in the mouth region, humans use it and respond rapidly; and, when it's not informative, they have to rely on the more robust but more sluggish luminance information in the eye-eyebrow region.

Introduction
Which face cues are we using for gender discrimination? Up until now, the small body of studies on this topic has highlighted the importance of the eyes, the eyebrows, the jaw and the face outline (e.g., Brown & Perrett, 1993; Nestor & Tarr, 2008a, 2008b; Russell, 2003, 2005; Yamaguchi, Hirukawa, & Kanazawa, 1995). Using Bubbles, Schyns, Bonnar, and Gosselin (2002; see also Gosselin & Schyns, 2001) found that relatively coarse eye and mouth information (5.62–22.5 cycles per face width for a face width subtending about 4 cycles per degree of visual angle) were significantly correlated with gender discrimination in humans. Relatedly, the distance between the brows and the upper eyelid was identified as the most reliable relational cue to gender in facial images (Burton, Bruce, & Dench, 1993; Campbell, Benson, Wallace, Doesbergh, & Coleman, 1999). Experiments investigating the role of pigmentation cues showed that human observers could rely on chromatic information—mostly on the red-green axis—to categorize gender especially when minimal discriminative shape information were revealed (Bruce & Langton, 1994; Hill, Bruce, & Akamatsu, 1995; Tarr, Kersten, Cheng, & Rossion, 2001; Tarr, Rossion, & Doerschner, 2002). The regions surrounding the eyes and the mouth were also found to be the most determinant chromatically (Nestor & Tarr, 2008b). 
All the studies cited above suffer from at least one of the following three potentially serious limitations on external validity. First, all of them—except Gosselin and Schyns (2001), Nestor and Tarr (2008a), and Schyns et al. (2002)—manipulated specific features and regions of the face with techniques such as morphing and caricaturing. These manipulations could have distorted the natural characteristics of authentic faces. Moreover, selective manipulation of these features might have biased the results toward a limited sample of all the facial information available. Second, the face stimuli used in all of these studies—except the studies performed by Tarr and colleagues—were grayscale pictures or they were controlled for different aspects (e.g., hair and ears removed, no makeup). In fact, the skin and hair reflectance properties of males and females differ (makeup only exaggerates these spectral dimorphism—Russell, 2003) and, as we have mentioned above, human observers can use these differences reliably. Third, all of these studies—except Nestor and Tarr (2008a)—used a small set of faces that needed to be shown many times to each participant. This context is likely to have promoted perceptual learning of the faces. Therefore, the results might reflect the peculiarities of the stimulus set rather than general characteristics of gender dimorphism. In fact, the repetition of the same face identity allows the subject to use a face identification strategy rather than a gender discrimination strategy. This may have artificially increased the role of eye region, a potent feature for face recognition (Gosselin & Schyns, 2001; Schyns et al, 2002; Sekuler, Gaspar, Gold, & Bennett, 2004). 
Here, we reassess the importance of facial cues for gender discrimination in a more realistic setting: We apply Bubbles—a technique that minimizes bias toward specific facial features and does not distort stimuli—to a set of 300 color images of Caucasian faces that were presented only once to 30 participants. 
Methods
Subjects
Thirty students from the University of Montreal and McGill University were recruited to participate to the experiment. Participants were between 20 and 30 years of age. They all had normal, or corrected to normal vision. Informed consent was obtained before the beginning of the experiment and a monetary compensation was provided. 
Stimuli
Stimuli were generated from 300 color images of Caucasian faces (150 females), chosen on Internet with the intent of ecological representativity. The only other characteristics required for selection were a clear gender membership, a neutral expression and a frontal view. Thus, no special attention was paid to lighting, file format, image size, age of depicted individual, etc. Subsequent transformations applied on the images were also kept to a minimal. Rotations, scalings, and translations in the image plane were applied to the face photographs in order to minimize the distance between handpicked landmarks around the eyes (4 landmarks each), the eyebrows (2 landmarks each), the nose (4 landmarks) and the mouth (4 landmarks). The average interpupil distance was 40 pixels (1.03 deg of visual angle). Note that these affine transformations do not modify the relative distances between features. Six instances of the resulting face images are shown on Figure 1a
Figure 1
 
(a) Three women and three men from our face database; and the average of all 150 women and 150 men from our face database. (b) A stimulus is generated by overlaying an opaque mid-gray mask punctured by a number of randomly located Gaussian apertures on a face.
Figure 1
 
(a) Three women and three men from our face database; and the average of all 150 women and 150 men from our face database. (b) A stimulus is generated by overlaying an opaque mid-gray mask punctured by a number of randomly located Gaussian apertures on a face.
Stimuli were created by sampling the face images subtending 3.28 deg of visual angle by presenting them behind an opaque mask punctured by an adjustable number of randomly located Gaussian apertures having a standard deviation of 4 pixels or 0.1 deg of visual angle (henceforth called ‘ bubble mask’). The result, shown in Figure 1b, is a sparsely sampled face on a mid-gray background. 
Apparatus
The experimental programs were run on a Macintosh G4 in the Matlab environment, using functions from the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997). All stimuli were presented on a Sony Trinitron monitor (1024 × 768 pixels at a refresh rate of 85 Hz). We determined the relationship between RGB values and luminance levels (measured with a Samsung SyncMaster 753 df photometer) for each color channel independently; the three best-fitted “gamma” functions were used in the computation of image statistics. Participants were seated in a dim ambient-lighted room at a distance of approximately 75 cm from the computer monitor. 
Procedure
Each participant was submitted to 300 trials and, importantly, each trial involved a different face. The presentation order of the 300 faces was randomized. In a given trial, one stimulus—a sparsely sampled face—appeared at the center of computer monitor and remained there until the participant had indicated the gender of the stimulus by pressing a labeled keyboard key. No feedback was provided. The number of bubbles per image was adjusted on a trial-by-trial basis to maintain performance at 75% correct using QUEST (Watson & Pelli, 1983). 
Results and discussion
Participants used an average of 27.06 bubbles and responded correctly on 74.74% of the trials. The average response time was 1.63 sec. The correlation between response time and accuracy was −0.1216 ( p < 0.001). There was a slight bias toward responding “man” (52.18% of the trials, p < 0.01) rather than “woman”. No difference was observed between female and male participants (51.58% and 52.72%, ns). 
Linear classification image analyses
To uncover which facial cues led more often to accurate or faster correct gender discrimination, we performed two least-square multiple linear regressions: one between discrimination accuracies (predictive variable) and bubble masks (explanatory variable) and another between quartiles of response time on correct trials and bubble masks. 1 The outcome of these regressions are two 128 by 128 planes of regression coefficients which we call classification images (Eckstein & Ahumada, 2002; Gosselin & Schyns, 2004)2. To compute group statistics, we summed classification images across participants and smoothed the resulting group classification images with a Gaussian kernel having a standard deviation of 6.93 pixels. The statistical analysis was restricted to the area of the classification images that could contain face information; the complementary area, which was irrelevant to the task at hand, was used to estimate the mean and the standard deviation of the null distribution and to transform the group classification images into Z-scores. Any significant positive local divergence from uniformity in our group classification images would indicate that the corresponding part of the stimuli led to more accurate responses, or faster correct responses. We therefore conducted one-tailed Pixel tests (Chauvin, Worsley, Schyns, Arguin, & Gosselin, 2005) on the group classification images transformed into Z-scored (Sr = 3469; for accuracy: Zcrit = 3.7 and Zmax = 6.48; for response time: Zcrit = 3.5 and Zmax = 4.04; p < .05). The statistical threshold provided by this test corrects for multiple comparisons while taking the spatial correlation inherent to our technique into account. 
Figure 2 displays the average women (column 1) and men (column 2) overlaid with a contour-plot representation of the accuracy and correct response time classification images. The colored pixels enclosed by the dotted black lines are statistically significant: the region of the eyes and eyebrows lead to more accurate and faster correct gender discrimination; this eye-eyebrow region is wider and more bilaterally distributed in the correct response time classification image (row 1) than in the accuracy classification image (row 2); and facial cues leading to fast correct responses also included the mouth region as well as the space between the mouth and the nose. To better understand the relation between the mouth region and our measurements, we ran an additional least-square multiple linear regression between quartiles of response time on incorrect trials and bubble masks. No pixel was significant in the resulting classification image (not shown). 
Figure 2
 
Displays the average men (column 2) and women (column 1) superimposed on a contour plot of classification images derived from accuracy (row 1) and response time (row 2). The colored pixels enclosed by the dotted black lines are statistically significant ( p < .05).
Figure 2
 
Displays the average men (column 2) and women (column 1) superimposed on a contour plot of classification images derived from accuracy (row 1) and response time (row 2). The colored pixels enclosed by the dotted black lines are statistically significant ( p < .05).
Beyond linear classification images
The linear classification image analyses confirmed that the eye-eyebrow region contains the most important cues for gender discrimination. However, they do not allow to identify more precisely the nature of these reliable cues, at least not directly. For example, we could wonder if these cues are mostly red-green pigmentation cues, as proposed by Tarr and colleagues? It's not so much a limit of the methodology than a limit of the search space we chose to explore—image location. In fact, Nestor and Tarr (2008b) have used classification images to probe the use of color directly during gender discrimination. On each one of 20,000 trials, color noise was added to the same androgynous morph and participants had to decide whether it looked more like a man or a woman. If we cannot address the color question directly, we can provide—based on the 300 faces of our face set—image statistics about the discriminative color information that was available within the eye-eyebrow region. 
We converted these face images to Lab color space because its channels represent perceptually relevant color opponent processes: L corresponds to the light-dark process, a to the red-green process, and b to the yellow-blue process. Then, we computed d′ on each pixel of the three Lab channels—we will call the resulting d′ planes color maps. This metric could be interpreted as the information available in a given pixel of a given color map to discriminate the gender. More specifically, a pixel's d′ is the distance (in standard deviation units) between the mean of the distribution of this pixel's value for male faces and the mean of the distribution of this pixel's value for female faces. The three color maps are represented as contour plots in Figure 3. Color lines delimit isovalued d′ corresponding to percentiles of 95%, 85%, and 75%. Warm colors were used for regions where men are lighter, redder or yellower than women; and cold colors for regions where men are darker, greener or bluer than women. To help with interpretation, the contour plots were placed over an image of the average men (column 1) and women (column 2). Thick dotted lines were added to delineate the significant regression coefficients found in the accuracy (white) and the correct response time classification images (black). 
Figure 3
 
Contour plots of the color maps superimposed to the average man (column 1) and woman (column 2). Dotted lines define clusters significantly correlated with accurate (white) and correct fast responses (black). The contour plot summarizes the spatial modulation of available information ( d's) in the dark-light (row 1), red-green channel (row 2) and yellow-blue (row 3) channels. The color-labeled lines of isovalued d's correspond to percentile 95%, 85%, 75%, 25%, 15%, and 5%.
Figure 3
 
Contour plots of the color maps superimposed to the average man (column 1) and woman (column 2). Dotted lines define clusters significantly correlated with accurate (white) and correct fast responses (black). The contour plot summarizes the spatial modulation of available information ( d's) in the dark-light (row 1), red-green channel (row 2) and yellow-blue (row 3) channels. The color-labeled lines of isovalued d's correspond to percentile 95%, 85%, 75%, 25%, 15%, and 5%.
The light-dark color map depicts the information that has been mainly investigated in the literature so far. It shows the availability of prominent gender cues in the temporal side of the brows and the eyes, over the upper lip, and under the commissure of the chin and the lower lip (Russell, 2003, 2005). Note also the luminance information located on the face outline near the cheeks. On average, this channel has higher d's than the other color channels (mean d's: light-dark = 0.36, red-green = 0.27, yellow-blue = 0.21). This set of informative features overlap substantially with the features found in the accuracy and correct response time classification images. The most informative pixels in the red-green color map—the second most informative color channel—are localized on the lips but are also distributed on the maxilla region and near the chin-lower lip commissure. The upper lip is a feature also found in the correct response time classification image. In comparison, the yellow-blue channel contains less information allowing to distinguish males from females. The most informative yellow-blue cues are clustered on the temporal sclera, on the nasal side of the brows and on the outer portion of the hair. None of these features is found in the classification images. 
Another cue that has already been targeted as the one of the most discriminative information for gender categorization is the eyelid-brow distance (Burton et al., 1993; Campbell et al., 1999), i.e. the distance between the center of the upper eyelid and the center of the bottom part of the eyebrow. If the participants used this cue they needed to see part of the eye, the eyelid and the brow together. Therefore, the performance observed in the trials in which these regions were presented together (see Table 1, first row) should be higher than the performance predicted by the linear combination of these regions presented individually with the appropriate weights from the accuracy classification image (see Table 1, second row). 
Table 1
 
The first row shows the mean accuracy observed when areas are revealed separately (columns 1–3) and together (column 4). The second row indicates the average accuracy predicted from linear regression. The last row displays the number of trials that were used to compute these statistics.
Table 1
 
The first row shows the mean accuracy observed when areas are revealed separately (columns 1–3) and together (column 4). The second row indicates the average accuracy predicted from linear regression. The last row displays the number of trials that were used to compute these statistics.
Eyes Brows Eyelids Eyes, brows and eyelids
Observed accuracy 0.7216 0.7329 0.6897 0.7626
Predicted accuracy 0.6082 0.7471 0.6612 0.8216
N 194 87 307 269
In fact, predictions made from the accuracy linear regression explains the performance observed when the eye, the eyelid and the brow are seen together. Moreover, image statistics computed on the 300 faces from our database indicate that this relational cue provide little discriminative information: the d′ of the eye-eyelids distance—measured from handpicked landmarks—is 0.91. In sum, these results do not support the use of the eyelid-brow distance in our experiment. Further analyses would be required to assess the use of other distance cues. However, Nestor and Tarr (2008a) performed a similar analysis on all pair wise conjunctions between the forehead, the eyes, the ears, the upper and lower part of the nose, the cheeks, the mouth, and the chin, and failed to found evidence for nonlinear use of information during their gender discrimination task. 
Conclusion
Which face cues do we use for gender discrimination? In this paper, we addressed this question in a more realistic setting than previous studies on the same topic. First, the face stimuli that have been used typically in gender discrimination experiments were grayscale photographs, normalized and controlled for different aspects. Our results can be considered as more representative of genuine gender discrimination because our face stimuli were real-life color photographs and, therefore, were not (artificially) controlled for luminance, chrominance, background, hair and makeup. Second, previous studies on facial gender discrimination cues used a small set of faces that needed to be shown many times to each participant; therefore, the results might reflect the peculiarities of small stimulus sets overlearned by participants rather than general characteristics of gender dimorphism. We used a set of 300 face photographs that were presented only once to each of our 30 participants. Third, gender discrimination studies typically manipulated specific features and regions of the face with techniques such as morphing and caricaturing. These manipulations probably altered the natural characteristics of faces, and biased the results. We sampled unaltered face photographs with minimum bias by presenting them behind mid-gray opaque masks punctured by a number of randomly located Gaussian apertures sufficient to maintain a 75% correct response rate. This sampling technique makes no assumption regarding feature processing—holistic or not. A comparison of the power spectrum of the 300 face photographs and the 9,000 face stimuli presented to participants (with bubbles) revealed a slight reduction of energy below 2.63 cycles per face width. This bias is unlikely to have interfered with normal face processing (e.g., Ruiz-Soler & Beltran, 2006). 
To uncover which facial cues led more often to accurate or faster correct gender discrimination, we performed three classification image analyses: on accuracies, on correct response times, and on incorrect response times. The accuracy classification image confirmed that the eye-eyebrow region is the most important for gender discrimination. We do not know if participants used facial features, or makeup, or trimmed eyebrows within this region to perform the task. In any case, this main result is in agreement with previous findings obtained using different methods (e.g., Brown & Perrett, 1993; Russell, 2003, 2005; Yamaguchi et al., 1995). 
Linear predictions made on a sub-set of our trials showed that participants did not use the eyelid-brow distance information, a distance cue that Bruce et al. (1993) and Campbell et al. (1999) proposed was one of the most reliable for gender discrimination. In fact, we discovered that the eyelid-brow distance has a small signal-to-noise ratio for gender discrimination. 
We computed image statistics on the Lab channels of all 300 faces of our face set to capture the color information available to resolve the task. Our color maps do not inform us about the morphological or spectral gender dimorphisms of the real world. Nevertheless, they show, for example, that the highly discriminative information contained in the eye-eyebrow area is mostly concentrated in the light-dark channel. This suggests that humans discriminate face gender based on a linear combination of luminance cues within the eye-eyebrow region. There is no inconsistency between our results and Tarr and colleagues' results about the important role of color in face gender discrimination (Nestor & Tarr, 2008a, 2008b; Tarr et al., 2001, 2002). They showed that participants relied on pigmentation cues (especially from the red-green channel) when minimal or no luminance information is available. Similarly, Yip & Sinha (2002) showed that color cues play a role in face identification when shape attributes are degraded. Yip and Sinha proposed that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation; and the same could be proposed about face gender discrimination. Parametric models aiming at automatic segmentation of facial features also focus on color information for the extraction of the lips (Evano, Clapier, & Coulon, 2004). 
That being said, the correct response time classification image along with the additional analysis on incorrect response time suggest a more ubiquitous role for facial color during gender discrimination. The mouth region is significantly correlated with correct fast responses (but not with incorrect fast responses) and the most discriminative information in the mouth region is concentrated in the red-green channel. This suggests that humans do use chromatic cues for discriminating face gender: When it's informative, they use it and respond rapidly (for evidence that color is perceived faster than shape, see Holcombe & Cavanagh, 2001; Moutoussis & Zeki, 1997a, 1997b); when it's not, they have to rely on the more robust and more sluggish luminance cues. The infero-temporal cortex, which is involved in both face perception and color perception (Clark et al., 1997; Edwards, Xiao, Keysers, Földiák, & Perrett, 2003), provides the ideal locus for such a dual strategy. 
Acknowledgments
This research was supported by FQRNT scholarship awarded to Nicolas Dupuis-Roy and Isabelle Fortin, and by NSERC and NATEQ grants awarded to Frédéric Gosselin. 
Commercial relationships: none. 
Corresponding author: Frédéric Gosselin. 
Email: frederic.gosselin@umontreal.ca. 
Address: Université de Montréal, C.P. 6128, succ. Centre-ville, Montréal, QC, H3C 3J7, Canada. 
Footnotes
Footnotes
1  For the least-square multiple linear regression on accuracy, the computations reduce to subtracting the mean of the bubble masks that led to an incorrect response from the mean of the bubbles masks that led to a correct response. And, for the regression on response time, the computations reduce to summing 1.5 times the mean of the bubble masks that led to a correct response and to a response time in the fastest quartile, 0.5 times the mean of the bubble masks that led to a correct response and to a response time in the second quartile, −0.5 times the mean of the bubble masks that led to a correct response and to a response time in the third quartile, and −1.5 times the mean of the bubble masks that led to a correct response and to a response time in the slowest quartile. Prior to these computations, every bubble mask was transformed into z-scores to give equal weight to all bubble masks. See Chauvin et al. (2005) for technical details.
Footnotes
2  Bubbles and reverse correlation experiments (e.g., Sekuler et al., 2004) result typically in linear classification images. However, the two techniques should not be confused. In a Bubbles experiment, the stimuli are sampled using multiplicative noise (or bubble masks) whereas, in a reverse correlation experiment, the stimuli are masked using additive noise. This apparently minor procedural difference has important functional consequences (e.g., Gosselin & Schyns, 2002; Murray & Gold, 2004).
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Figure 1
 
(a) Three women and three men from our face database; and the average of all 150 women and 150 men from our face database. (b) A stimulus is generated by overlaying an opaque mid-gray mask punctured by a number of randomly located Gaussian apertures on a face.
Figure 1
 
(a) Three women and three men from our face database; and the average of all 150 women and 150 men from our face database. (b) A stimulus is generated by overlaying an opaque mid-gray mask punctured by a number of randomly located Gaussian apertures on a face.
Figure 2
 
Displays the average men (column 2) and women (column 1) superimposed on a contour plot of classification images derived from accuracy (row 1) and response time (row 2). The colored pixels enclosed by the dotted black lines are statistically significant ( p < .05).
Figure 2
 
Displays the average men (column 2) and women (column 1) superimposed on a contour plot of classification images derived from accuracy (row 1) and response time (row 2). The colored pixels enclosed by the dotted black lines are statistically significant ( p < .05).
Figure 3
 
Contour plots of the color maps superimposed to the average man (column 1) and woman (column 2). Dotted lines define clusters significantly correlated with accurate (white) and correct fast responses (black). The contour plot summarizes the spatial modulation of available information ( d's) in the dark-light (row 1), red-green channel (row 2) and yellow-blue (row 3) channels. The color-labeled lines of isovalued d's correspond to percentile 95%, 85%, 75%, 25%, 15%, and 5%.
Figure 3
 
Contour plots of the color maps superimposed to the average man (column 1) and woman (column 2). Dotted lines define clusters significantly correlated with accurate (white) and correct fast responses (black). The contour plot summarizes the spatial modulation of available information ( d's) in the dark-light (row 1), red-green channel (row 2) and yellow-blue (row 3) channels. The color-labeled lines of isovalued d's correspond to percentile 95%, 85%, 75%, 25%, 15%, and 5%.
Table 1
 
The first row shows the mean accuracy observed when areas are revealed separately (columns 1–3) and together (column 4). The second row indicates the average accuracy predicted from linear regression. The last row displays the number of trials that were used to compute these statistics.
Table 1
 
The first row shows the mean accuracy observed when areas are revealed separately (columns 1–3) and together (column 4). The second row indicates the average accuracy predicted from linear regression. The last row displays the number of trials that were used to compute these statistics.
Eyes Brows Eyelids Eyes, brows and eyelids
Observed accuracy 0.7216 0.7329 0.6897 0.7626
Predicted accuracy 0.6082 0.7471 0.6612 0.8216
N 194 87 307 269
© 2009 ARVO
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