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# The Influence of Facial Width-to-Height Ratio on Micro-expression Recognition

Siwei Zhang, Jinyuan Xie, Qi Wu

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Siwei Zhang, Jinyuan Xie, Qi Wu. The Influence of Facial Width-to-Height Ratio on Micro-expression Recognition. 2nd International Conference on Intelligence Science (ICIS), Nov 2018, Beijing, China. pp.265-272, 10.1007/978-3-030-01313-4_28. hal-02118823

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# The Influence of Facial Width-to-Height Ratio on Micro-expression Recognition

Siwei Zhang*, Jinyuan Xie*, Qi Wu¹*

*Cognition and Human Behavior Key Laboratory of Hunan Province, Department of Psychology, Hunan Normal University
Changsha, China
sandwich624@yeah.net

**ABSTRACT.** The aim of the present study was to uncover the potential impact of facial width-to-height ratio (fWHR) on micro-expression and macro-expression recognition. The JACBART paradigm was used for the presentation of facial expressions. Participants were asked to recognize six kinds of basic expressions (sadness, surprise, anger, disgust, fear, happiness) on high fWHR faces or on low fWHR faces under 67ms, 333ms and 600ms duration conditions respectively. The results indicated that, the fWHR did not affect the recognition of macro-expressions which were presented for 600ms in the present study, but the fWHR could influence the recognition accuracy of micro-expressions of surprise and happiness. Specifically, participants could identify the facial expression of surprise more effectively on high fWHR faces than on low fWHR faces under the condition of 67ms. And participants also could recognize the facial expression of happiness more accurately on high fWHR faces in the conditions of 67ms and 333ms. These results revealed the facial expressions of happiness and surprise on high fWHR faces may have an early processing advantage in micro-expression recognition. And the result also demonstrated that individuals spontaneously use fWHR as a clue to recognize micro-expressions.

**KEYWORDS:** emotion and affection, mechanism of cognition, essence of perception, facial width-to-height ratio, micro-expression, micro-expression recognition

## 1 Introduction

We often say a person should never judge a book by its cover. But in real life, we usually make a judgment about his/her age or personal character, according to individuals' appearance, especially the facial features, when we see unfamiliar faces on the moment. Humans are very good at using other people's facial structures to interpret their nonverbal signals, like facial expressions, helping themselves to achieve a better social interaction.

Indeed, numerous studies in the past have shown that the facial features of the expressers, such as eyes, mouths, and other facial structures (Blais et al., 2012; Deska et al., 2017; Sacco et al., 2009), can influence facial expression recognition. The present
study focused on whether individuals can identify fleeting micro-expressions effec-
tively through other people's facial structures.

Facial width-to-height ratio (fWHR) is a stable facial structure. A number of previ-
ous studies investigated the predictive effects of fWHR on individual traits and behav-
iors (Geniole et al., 2015; Haselhuhn et al., 2015). For instance, fWHR can influence
individual's facial expression recognition (Deska et al., 2017). But no studies have ex-
amined the impact of fWHR on micro-expression recognition. Therefore, we used the
six basic facial expressions (happiness, sadness, anger, fear, disgust, surprise) on high
fWHR faces or on low fWHR faces made by Facegen Modeller (www-facegen.com),
to investigate the effect of fWHR on facial expression recognition under different du-
ration conditions (67ms, 333ms, 600ms).

1.1 Micro-expressions of emotion

Individual’s emotion is impacted by the unconscious impulses which stems from
the brain's mood centers (Frank et al., 2015), so that the control of body by emotion is
stronger than that by logical thinking in some cases. The expression of emotions can
be indicated by changes in facial expression, body posture, and other manners (Ek-
man et al., 1983).

Micro-expressions and macro-expressions are both the manifestation of one's inter-
nal emotion. Macro-expression is a direct expression of an 'emotion', which is the most
common expression in our daily life and it usually lasts for 1/2s to 4s at least (Yan et
al., 2013). It is also easy to be controlled and falsified. But the micro-expression is the
barometer of authentic human emotional activities and can help people to spot the lies
(Frank et al., 2015; Vrij et al., 2014). Micro-expressions are actually very short and
uncontrollable facial expressions that show up when human beings are trying to sup-
press or hide real emotions (Ekman 2009; Yan et al., 2013). More specifically, micro-
expressions usually last no longer than 1/2s (Matsumoto et al., 2011; Yan et al., 2013),
and can often be neglected during the daily conversations (Frank et al., 2015). In gen-
eral, they occur under stressful situations where people want to control the feeling of
fear and guilty, such as lying (Porter et al., 2008). In such pressure circumstances, the
ability to better recognize micro-expression may be indispensable. In order to read mi-
cro-expressions correctly, a Micro-Expression Training Tool (METT) has been devel-
oped (Ekman 2002; Hurley et al., 2014).

Currently, micro-expression recognition has been widely applied in the field of
medicine, political psychology, national security and justice (ten Brinke et al., 2012;
Shen et al., 2012; Weinberger 2010). Even in the field of education, some researchers
found that the changes of micro-expression can be used as effective indicators to meas-
ure the intellectual level and the knowledge conceptual paradox of students. It can also
be used to monitor the teaching efficiency (Chiu et al., 2014). However, due to its char-
acteristics, micro-expression recognition is challenging for humans and the recognition
accuracy is about 45~59% (Matsumoto et al., 2011). Therefore, it is advisable for re-
searchers to develop automatic micro-expression recognition tools (e.g., Guo et al.,
2017; Wu et al., 2011) and investigating the potential factors that affect the recognition
of micro-expressions in humans would help us to better achieve that purpose.

1.2 Facial Width-to-Height Ratio (fWHR)

As an anthropometric measure of facial shape, fWHR is calculated by measuring
the ratio between bizygomatic width and the distance from the upper lip to the mid-
brow (Hehman et al., 2015; Weston et al., 2007). The higher the fWHR, the wider the
face. The smaller the fWHR, the longer the face (Haselhuhn et al., 2012). Abundant
studies found that high fWHR is associated with aggression and dominance (Geniole
et al., 2015). Meta-analytic evidence also strongly indicate that there is a positive rela-
tionship between high fWHR and aggression traits (Haselhuhn et al., 2015).

Furthermore, there were findings suggested that the anger — the state most closely
related to aggression — increased fWHR (Marsh et al, 2014). And some researchers
also found that high fWHR may share phenotypic overlap with angry expressions
(Deska et al., 2017). The recent evidence also showed that fWHR significantly influ-
ences the recognition of macro-expressions of anger, fear and happiness (Deska et al.,
2017). For example, researchers demonstrated that when neutral expressions were pre-
sented, high fWHR and low fWHR neutral expression were respectively perceived as
signaling anger and fear. When individuals' faces display emotional expressions, such
as happiness, fear and anger, fWHR can bias ascriptions of emotion by facilitating the
recognition of fear and happiness on low fWHR faces and increasing the recognition of
anger on faces with high fWHR (Deska et al., 2017).

2 The current study: effects of Facial Width-to-Height Ratio on the recognition micro-expressions

Macro-expression is an important source of social interactions that appear over a
single or various regions of the face. It is easily recognized by human. Like macro-
expression, micro-expression has three rendering stages: onset, peak and offset (Yan et
al., 2013). But unlike macro facial expressions, micro-expression displays more rapidly
and transiently (less than 1/2s) which makes it hard to be identified. And few can fake
a micro-expression (Frank et al., 2015).

Macro-expression recognition has already been studied a lot by researchers. We
found that macro facial expression recognition is easily to be influenced by our facial
structure, such as larger eyes facilitated the recognition of fearful facial expressions
(Sacco et al., 2009). The latest study also found that the fWHR can influence the recog-
nition of macro facial expression of anger, fear and happiness (Deska et al., 2017).
Previous study has also illustrated that the recognition of micro-expression is affected
by facial features, such as the mouth movements of a face inhibited conscious detection
of all types of micro-expressions in that face (Iwasaki et al., 2016). But it is unclear
whether the fWHR can also influence the recognition of micro-expressions. Therefore, the present study investigated the relationship between fWHR and the recognition of micro-expressions and explored the differential influence of fWHR on macro-expression and micro-expression recognition.

# 3 Method

## 3.1 Participants and design

We used the Gpower software (V3.1; Faul et al., 2007) to estimate our sample size. This analysis suggested we collect at least 168 participants to obtain 0.8 power. Consequently, one hundred sixty-eight undergraduate students as participants were recruited from Hunan Normal University. All the participants were Chinese, M<sub>age</sub> = 21.75, SD = 1.75, 47.6% female. Participants had normal or corrected-to-normal vision and never had experience with such an experiment. After the experiment, the participants were given partial course credit or monetary reward.

A 2 (fWHR: high fWHR, low fWHR) × 3 (duration: 67ms, 333ms, 600ms) × 6 (facial expression: sadness, surprise, anger, disgust, fear, happiness) mixed-model experimental design was used, with fWHR being the between-subjects factor while facial expression and duration being the within-subjects factors.

## 3.2 Stimuli

Emotion images used in the practice trials were from Nimstim database (Tottenham et al., 2009), which could help participants to get familiar with the experimental procedure. In the formal test, we used the FaceGen Modeller software to generate 24 models (12 females, 12 males; age 21-30) and transformed them into images of seven facial expressions (neutral, sadness, surprise, anger, disgust, fear, happiness) with high fWHR faces (fWHR > 1.9) and low fWHR faces (fWHR < 1.7) (Chae et al., 2014; Haselhuhn et al., 2012). The height of faces from one person were consistent. And the intensity of basic expressions was set to maximum (1). The number of images was 336 in total, the size of each image was 400 × 400 pixels.

# 4 Apparatus and procedure

Each participant was randomly assigned to the high fWHR face group (n = 84) or the low fWHR face group (n = 84). Before the formal test, participants were familiarized with the procedure and finished six practice trials. The stimulus was presented centrally with a 60-Hz refresh rate on a laptop. The E-Prime software controlled stimulus presentation and data collection. In the practice trials, six images of basic expressions would be presented. In the formal test, the duration of expression was divided into three conditions (67ms, 333ms and 600ms) which consisted of 144 trials, with 48 trials per condition. The 24 models were randomly assigned to three duration conditions, with 8 models per condition. The order of the combination of the models and the duration
conditions was counterbalanced across participants by balanced Latin square. In each condition, the six micro-expressions of each model were randomly displayed.

This study adopted the JACBART paradigm (Ekman 2002; Svetieva et al., 2016) to present the facial expressions. Firstly, a fixation cross (500ms) was presented in the center of the screen, then the facial expression image was sandwiched in between two 1000ms presentations of the same model's neutral expression. Facial expression images randomly presented for 67ms, 333ms or 600ms. When the facial expression disappeared, participants were asked to choose one of six emotion expression labels (i.e., sadness, surprise, anger, disgust, fear, happiness) and “none of the above” from a list to describe the expression on the screen (pressing one of seven buttons 1-7 on a response keyboard). The next trial was presented after the participants made the judgment (unlimited reaction time).

## 5 Results

We conducted a 2 (fWHR: high fWHR, low fWHR) × 3 (duration: 67ms, 333ms, 600ms) × 6 (facial expression: sadness, surprise, anger, disgust, fear, happiness) analysis of variance (ANOVA) on recognition accuracy. The analysis yielded a significant main effect on facial expression [F (5, 830) = 356.93, *p* < 0.01, *η*<sub>p</sub><sup>2</sup> = 0.68], and the duration [F (2, 332) = 125.39, *p* < 0.01, *η*<sub>p</sub><sup>2</sup> = 0.43]. Generally, the recognition accuracy of happy, surprise and sad expressions was higher than other expressions in three duration conditions, and the recognition accuracy of all facial expressions in the condition of 600ms duration was much higher than the other two duration conditions. Descriptive statistical results were shown in Table 1. The main effect of fWHR was not significant [F (1, 166) = 2.33, *p* > 0.05, *η*<sub>p</sub><sup>2</sup> = 0.01]. The interaction between facial expression and duration was significant [F (10, 1660) = 15.15, *p* < 0.01, *η*<sub>p</sub><sup>2</sup> = 0.08]. However, there were no significant interactions of fWHR × facial expression and fWHR × duration [F (5, 830) = 0.86, *p* > 0.05, *η*<sub>p</sub><sup>2</sup> = 0.01; F (2, 332) = 1.26, *p* > 0.05, *η*<sub>p</sub><sup>2</sup> = 0.01]. The three-way interaction among the three factors was significant [F (10, 1660) = 2.15, *p* < 0.05, *η*<sub>p</sub><sup>2</sup> = 0.01].

<table><thead><tr><th rowspan="3"></th><th colspan="2">67ms</th><th colspan="2">67ms</th><th colspan="2">333ms</th><th colspan="2">333ms</th><th colspan="2">600ms</th><th colspan="2">600ms</th></tr><tr><th colspan="2">High fWHR</th><th colspan="2">Low fWHR</th><th colspan="2">High fWHR</th><th colspan="2">Low fWHR</th><th colspan="2">High fWHR</th><th colspan="2">Low fWHR</th></tr><tr><th>M</th><th>SD</th><th>M</th><th>SD</th><th>M</th><th>SD</th><th>M</th><th>SD</th><th>M</th><th>SD</th><th>M</th><th>SD</th></tr></thead><tbody><tr><td>sad-</td><td>0.69</td><td>0.33</td><td>0.71</td><td>0.35</td><td>0.75</td><td>0.34</td><td>0.74</td><td>0.34</td><td>0.79</td><td>0.31</td><td>0.75</td><td>0.33</td></tr><tr><td>sur-</td><td>0.72</td><td>0.26</td><td>0.64</td><td>0.25</td><td>0.78</td><td>0.21</td><td>0.73</td><td>0.24</td><td>0.76</td><td>0.23</td><td>0.77</td><td>0.23</td></tr><tr><td>an-</td><td>0.12</td><td>0.17</td><td>0.14</td><td>0.19</td><td>0.16</td><td>0.21</td><td>0.13</td><td>0.20</td><td>0.12</td><td>0.19</td><td>0.12</td><td>0.19</td></tr><tr><td>dis-</td><td>0.07</td><td>0.13</td><td>0.08</td><td>0.14</td><td>0.09</td><td>0.20</td><td>0.09</td><td>0.19</td><td>0.08</td><td>0.17</td><td>0.11</td><td>0.19</td></tr><tr><td>fear</td><td>0.40</td><td>0.27</td><td>0.40</td><td>0.29</td><td>0.59</td><td>0.33</td><td>0.54</td><td>0.34</td><td>0.60</td><td>0.30</td><td>0.57</td><td>0.33</td></tr><tr><td>happ-</td><td>0.78</td><td>0.22</td><td>0.67</td><td>0.27</td><td>0.90</td><td>0.18</td><td>0.83</td><td>0.20</td><td>0.90</td><td>0.17</td><td>0.85</td><td>0.22</td></tr></tbody></table>
<caption>**Table 1.** Descriptive statistical results from this study</caption>

Further simple effects analysis revealed that, participants were more sensitive to the facial expressions of happiness and surprise on high fWHR faces than on low fWHR faces under the condition of 67ms [$F$ (1, 166) = 8.31, *p* < 0.01, $\eta_p^2$ = 0.05; $F$ (1, 166) = 4.14, *p* < 0.05, $\eta_p^2$ = 0.02]. But under the condition of 67ms, the fWHR did not influence the recognition of other micro-expressions (i.e., sadness, anger, disgust, fear) [$Fs$ < 1, *ps* > 0.43]. In addition, under the condition of 333ms, the recognition accuracy of the expression of happiness was higher on high fWHR faces than on low fWHR faces [$F$ (1, 166) = 5.11, *p* < 0.05, $\eta_p^2$ = 0.03]. But the fWHR also did not influence the recognition of other micro-expressions (i.e., sadness, surprise, anger, disgust, fear) [$Fs$ < 1.8, *ps* > 0.19]. The fWHR also did not influence the recognition of macro-expression in the condition of 600ms (i.e., sadness, surprise, anger, disgust, fear, happiness) [$Fs$ < 3.4, *ps* > 0.07].

# 6 Discussion

This study compared the recognition of facial expression with 6 basic expressions on high fWHR faces or on low fWHR faces under different duration conditions (67ms, 333ms, 600ms), and investigated the differential influence of facial width-to-height ratio on macro and micro expressions. To be specific, the duration of expression did influence the recognition of facial expression, which was consistent with previous studies (Shen et al., 2012). And the results also showed that, the fWHR did not affect the recognition of macro expression which was presented for 600ms in the present study. In contrast, the fWHR influenced the micro-expressions of happiness and surprise. More specifically, participants identified the facial expression of surprise more effectively on high fWHR faces than on low fWHR faces under the condition of 67ms. And participants also recognized the facial expression of happiness more accurately on high fWHR faces in the condition of 67ms and 333ms duration. It suggests that the expression of surprise and happiness on high fWHR faces may have an early advantage in micro-expression recognition.

A recent eye-tracking study indicated that individuals pay more attention to the eyes area in large faces, while the distribution of gaze towards small faces is still in the nose center (Wang 2018). The expression of surprise is accentuated by the eyes, such as raised eyebrows and wide eyes (Ekman 1978). In a very short period, individuals are likely to notice the eyes area first. So the micro-expression of surprise on high fWHR face may be more likely to be unconsciously processed by individuals. The reason for fWHR can influence the recognition of happiness on high fWHR faces might be related to the processing characteristics of the expression of happiness. The expressed regions of happiness in face is mainly concentrated on the mouth, which is the prominent region to convey information (Blais et al., 2012). Studies have indicated that the lower face plays more important role than the upper face in micro-expression recognition (Iwasaki et al., 2016). So high fWHR means the lower face is large, the participants were more
likely to focus on the mouths, and thus were more accurately to recognize the expres-
sion of happiness. The present study, did not find a similar results as previous research
that the fWHR affects the recognition of macro-expression (Deska et al., 2017). How-
ever, in the previous studies (Deska et al., 2017), researchers had only provided two
labels (anger versus fear or happiness) for participants to choose. This procedure would
definitely overactive the concepts of anger thereby participants could easily establish a
link between fWHR and anger. But in the current experiment, participants were asked
to choose correct response from more than six expression labels. Such a design would
reduce the activation of emotional semantic concepts that may be related to fWHR. The
interaction between language and fWHR should be investigated in future works.

In summary, the results demonstrated that there are differences between the recog-
nition of micro-expressions and macro-expressions, and that fWHR can influence the
processing of micro-expression of happiness and surprise on high fWHR faces. The
current study only found that fWHR can affect the micro-expression recognition, but
the underlying mechanisms behind this phenomenon — such as how the fWHR changes
the processing of faces — have not been clarified yet. In addition, our participants
mainly were from China. More cross-cultural studies should be considered in future.

Acknowledgements

The authors wish to express sincere appreciation to Shengjie Zhao (Hunan Normal
University, Changsha, China) for his assistance with data collection. This work was
supported by the National Natural Science Foundation of China (grant number
31300870) and the Hunan Normal University (grant number 13XQN01 2015yx08).

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