Dynamics of facial actions for assessing smile genuineness

Comparison of pleasant and unpleasant smile characteristics in the perception of the laypeople in an Iranian population

INTRODUCTION

This study aimed to compare the characteristics of pleasant and unpleasant smiles from the perception of laypeople.

METHODS

Two-hundred posed smile photographs were obtained from adult participants with no anomaly, restoration, or severe crowding and spacing in anterior teeth. Photographs were shown to 26 judges to give each photograph a score for attractiveness. Upper and lower quartiles for the most and least rated smiles were determined, and variables including gingival display, smile arc, midline deviation, buccal corridor, smile width, tooth rotation or malposition, diastema, upper and lower vermilion show, and tooth form were measured. Independent-sample t test and Pearson chi-square analysis were used to determine the differences between the pleasant and unpleasant groups for quantitative and qualitative variables, respectively. The linear regression model revealed variables with the most significant impact on the mean score.

RESULTS

All quantitative and qualitative variables except tooth form significantly differed between pleasant and unpleasant smile groups (P <0.05). The consonant smile arc was associated with an increase of 12.59% in mean scores compared with the inconsonant smile arc. Each tooth reported with malposition was correlated with a decrease of 9.37% in the scores. In the same way, each 1-mm increase in midline diastema and occlusal cant coincided with a drop of 8.73% and 3.59% in scores, respectively.

CONCLUSIONS

The results of this study suggested that smile arc, tooth malposition, midline diastema, and occlusal plane canting had the most impact on smile esthetics and should be given priority by orthodontists in the treatment plan of choice.

Smile Reanimation with Masseteric-to-Facial Nerve Transfer plus Cross-Face Nerve Grafting in Patients with Segmental Midface Paresis: 3D Retrospective Quantitative Evaluation

Facial paresis involves functional and aesthetic problems with altered and asymmetric movement patterns. Surgical procedures and physical therapy can effectively reanimate the muscles. From our database, 10 patients (18–50 years) suffering from unilateral segmental midface paresis and rehabilitated by a masseteric-to-facial nerve transfer combined with a cross-face facial nerve graft, followed by physical therapy, were retrospectively analyzed. Standardized labial movements were measured using an optoelectronic motion capture system. Maximum teeth clenching, spontaneous smiles, and lip protrusion (kiss movement) were detected before and after surgery (21 ± 13 months). Preoperatively, during the maximum smile, the paretic side moved less than the healthy one (23.2 vs. 28.7 mm; activation ratio 69%, asymmetry index 18%). Postoperatively, no differences in total mobility were found. The activity ratio and the asymmetry index differed significantly (without/with teeth clenching: ratio 65% vs. 92%, p = 0.016; asymmetry index 21% vs. 5%, p = 0.016). Postoperatively, the mobility of the spontaneous smiles significantly reduced (healthy side, 25.1 vs. 17.2 mm, p = 0.043; paretic side 16.8 vs. 12.2 mm, p = 0.043), without modifications of the activity ratio and asymmetry index. Postoperatively, the paretic side kiss movement was significantly reduced (27 vs. 19.9 mm, p = 0.028). Overall, the treatment contributed to balancing the displacements between the two sides of the face with more symmetric movements.

Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD

Time is a key factor to consider in Autism Spectrum Disorder. Detecting the condition as early as possible is crucial in terms of treatment success. Despite advances in the literature, it is still difficult to identify early markers able to effectively forecast the manifestation of symptoms. Artificial intelligence (AI) provides effective alternatives for behavior screening. To this end, we investigated facial expressions in 18 autistic and 15 typical infants during their first ecological interactions, between 6 and 12 months of age. We employed Openface, an AI-based software designed to systematically analyze facial micro-movements in images in order to extract the subtle dynamics of Social Smiles in unconstrained Home Videos. Reduced frequency and activation intensity of Social Smiles was computed for children with autism. Machine Learning models enabled us to map facial behavior consistently, exposing early differences hardly detectable by non-expert naked eye. This outcome contributes to enhancing the potential of AI as a supportive tool for the clinical framework.