# Dissecting the smile: a spatio-temporal study

Félix Marcellin, Emilien Colin, François-Régis Sarhan, Stéphanie Dakpé

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Félix Marcellin, Emilien Colin, François-Régis Sarhan, Stéphanie Dakpé. Dissecting the smile: a spatio-temporal study. Multidisciplinary Biomechanics Journal, 2025, 49th congress of the Société de Biomécanique, 10.46298/mbj.14513. hal-04737995v2

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# Dissecting the smile: a spatio-temporal study

Félix Marcellin, PhD<sup>a, b*</sup>, Emilien Colin, PharmD, MSc<sup>a, c</sup>,
François-Régis Sarhan, PT, PhD<sup>a, b</sup>, Stéphanie Dakpé, MD,
PhD<sup>a, b, c</sup>

a CHIMERE - UR 7516 UPJV, Amiens, France

b Institut Faire Faces, Amiens, France

c Service de Chirurgie Maxillo-Faciale CHU Amiens-Picardie, Amiens, France

* Corresponding author: felix.marcellin@institut-faire-faces.eu

Received date: 05/04/2024

Accepted date: 28/06/2024

Publication date: 31/01/2025

**Keywords:** smile, cycle, spatiotemporal parameters, motion capture

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

The smile, an exquisite expression of human emotion, transcends cultural boundaries and speaks a universal language of warmth and connection. The facial expression of a smile is universally recognised as a signal of happiness or positive emotion in various cultures. Beyond its physiological manifestation, notably the release of endorphins and the reduction of stress hormones, which contribute to improved well-being (Dufner et al. 2010), the smile possesses immense psychological power, capable of lifting spirits, easing tensions and forging links between individuals. In history, art and literature, the smile has been celebrated as a timeless emblem of the human experience, immortalised in countless works of art and treasured souvenirs. At its core, the smile embodies the beauty of human connection and the resilience of the human spirit. But certain types of damage can disrupt the smile and the social function of those affected. Smile is most commonly analysed clinically in deficit situations, with facial palsy being the most prevalent pathological condition. Peripheral facial palsy refers to the partial or total loss of function in certain facial muscles due to damage to the facial nerve, which can have various aetiologies. Current methods of facial movement analysis rely on clinical assessment, but quantitative methods such as motion capture have been developed on an experimental basis (Sarhan et al. 2023). Several studies have attempted to overcome the lack of objective, reproducible, and sensitive measures for facial movement assessment using these methods

(Hontanilla et al., 2008; Sforza et al., 2010). After a validation of our method for the normalized displacement as feature of interest for healthy volunteers and patient follow-up (Sarhan et al. 2023, Marcellin et al. 2023, Rodriguez et al. 2023), the objective of our research is now to focus on time spatiotemporal parameters of facial mimicry and more precisely here to the smile to study if there are similarities between healthy subjects.

## 2. Methods

Twenty-three healthy subjects (7 men and 16 women) aged 24±8 years were recruited for this study. They were equipped with 105 Vicon passive markers (1.5 mm Ø) placed all over the face. Participants performed the smile movement without tempo instructions in front of a 10-camera Vicon Vantage V16 system recording at 100Hz. After labelling the points, including the labial commissure point (Dagnes et al., 2018), with reference to x,y and z displacements curves, the frame of the beginning of the movement (when the marker begins to move), the end of contraction (when the marker reaches its maximum amplitude), beginning of relaxation (the moment when the marker begins its return to its starting position), and the end of the movement (when the marker returns to its starting position) were marked on the Vicon Nexus timeline for each subject. The movements were normalized from 0 to 100% of a smile cycle using cubic spline interpolation. The mean and standard deviation of the time at which contraction ended and relaxation began were calculated
<figure><img src="image_2.png" /><figcaption>Figure 1. Mean displacement and phase limits for the smile</figcaption></figure>

and expressed as a percentage. The mean and standard
deviation of normalised displacement of markers in
amplitude and time was calculated. Finally, a distribu-
tion frequency analysis was carried out.

3. Results and discussion

<table><caption>**Table 1.** Distribution frequency analysis for the end of contraction phase and beginning of relaxation phase.</caption>
  <tbody>
    <tr>
      <th>% of smile cycle</th>
      <td>14</td>
      <td>16</td>
      <td>18</td>
      <td>20</td>
      <td>22</td>
      <td>24</td>
      <td>26</td>
      <td>28</td>
      <td>30</td>
      <td>32</td>
      <td>34</td>
      <td>36</td>
    </tr>
    <tr>
      <th>End-of-contraction frequency</th>
      <td>2</td>
      <td>2</td>
      <td>1</td>
      <td>3</td>
      <td>6</td>
      <td>1</td>
      <td>4</td>
      <td>0</td>
      <td>1</td>
      <td>2</td>
      <td>1</td>
      <td>0</td>
    </tr>
    <tr>
      <th>% of smile cycle</th>
      <td>58</td>
      <td>60</td>
      <td>62</td>
      <td>64</td>
      <td>66</td>
      <td>68</td>
      <td>70</td>
      <td>72</td>
      <td>74</td>
      <td>76</td>
      <td>78</td>
      <td>80</td>
      <td>82</td>
      <td>84</td>
      <td>86</td>
      <td>88</td>
      <td>90</td>
    </tr>
    <tr>
      <th>Frequency of onset of relaxation</th>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>4</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
    </tr>
  </tbody>
</table>

The mean value for the end of contraction phase is 22,4 ± 5,7 % and 80,39 ± 7,2% for the beginning of relaxation phase (Figure 1). Concerning the frequency of distribution, we can see that for the contraction phase, the highest frequency is at 22% of smile cycle and for relaxation there is equality between 80% and 86% (Table 1).

We can see that the three phases of the smile have the
same length for the smile despite the fact that there
were no tempo instructions when the movement was
performed.

For the 23 volunteers studied, the standard devia-
tions for the limit of these phases were 6% and 7%
of the smile cycle, showing little difference between
volunteers.

This result is in line with Özsoy et al. 2024. This work
shows the same duration for the phase than us.

At present, we have not found any other references on
the spatiotemporal analysis of smiles.

Despite the fact that the smile requested is not con-
strained to a tempo, it might be interesting to try to
record a spontaneous smile from the subject during the
acquisitions to compare it with our “on command” smile
To reinforce our study, we could extend the parame-
ters of interest to the speed of movement and also to
the order in which the markers are activated. Taken
together, these parameters will give us comprehensive
information about the temporality of smiling in healthy
volunteers, and could be compared with pathological
data, enabling patients to be better managed.

4. Conclusions

In this study, we describe the analysis of smile cycle
using motion capture. This spatio temporal information
coupled with displacement data can increase knowledge
of facial mimicry and continue to improve the man-
agement of patients suffering from facial pathologies
leading to motor problems on the face. It would be
interesting to analyse velocity and the order in which
the markers are activated, and to apply this method to
other movements and also to pathological movements to
study the differences with the healthy reference. Overall,
this study provides promising insights into the use of
biomechanical analysis to follow-up patients with facial
palsy. Finally, this study gives us promising prospects for
studying the biomechanics of facial mimicry, which is
a highly complex part of the human body but which
may show similarities in the healthy population.
## Funding

This research was funded the Fondation des Gueules Cassées (43-2023), the EquipEx FiGuRes (ANR-10-EQPX-0001) France 2030 program and the CPER MOSOPS project.

## Conflict of Interest Statement

The authors have no conflicts of interest to declare.

## References

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Dufner M, Brümmer M, Chung JM, Drewke PM, Blaison C, Schmukle SC. Does Smile Intensity in Photographs Really Predict Longevity? A Replication and Extension of Abel and Kruger (2010). *Psychol Sci.* 2018 Jan;29(1):147-153.

Hontanilla B, Aubá C. 2008. Automatic three-dimensional quantitative analysis for evaluation of facial movement. *Journal of Plastic, Reconstructive & Aesthetic Surgery*. 61(1):18-30.

Marcellin F, Rodríguez M E. A, Leturger H, Colin E, Sarhan F-R Dakpé S. 2023. Objective assessment of facial mimicry using motion capture for patient follow-up. Computer Methods in Biomechanics and Biomedical Engineering. *In edition*.

Özsoy Ö, Özsoy U, Yıldırım Y, Alkan E, Yılmaz B, Güllü SE. Correlation of 3D Morphometric Changes, Kinematics, and Muscle Activity During Smile. *Laryngoscope*. 2024 Jan 16. doi: 10.1002/lary.31289. Epub ahead of print. PMID: 38226662.

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Sarhan F, Olivetto M, Ben Mansour K, Neiva C, Colin E, Choteau B, Marie J, Testelin S, Marin F, Dakpé S. 2023. Quantified analysis of facial movement: A reference for clinical applications. *Clinical Anatomy*. 36(3):492-502.

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