• The korean journal of orthodontics
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• v.48 no.3
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• pp.182-188
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• 2018

Objective: This study was performed to validate the autonomous maximal smile (AMS) as a new reference for evaluating dental and gingival exposure. Methods: Digital video clips of 100 volunteers showing posed smiles and AMS at different verbal directives were recorded for evaluation a total of three times at 1-week intervals. Lip-teeth relationship width (LTRW) and buccal corridor width (BCW) were measured. LTRW represented the vertical distance between the inferior border of the upper vermilion and the edge of the maxillary central incisors. Intraclass correlation coefficients (ICCs) for reproducibility, and the m-value (minimum number of repeated measurements required for an ICC level over 0.75), were calculated. Results: LTRW and BCW of the AMS were 1.41 and 2.04 mm, respectively, greater than those of the posed smile (p < 0.05), indicating significantly larger dental and gingival exposure in the AMS. The reproducibility of the AMS (0.74 to 0.77) was excellent, and higher than that of the posed smile (0.62 to 0.65), which had fair-to-good reproducibility. Moreover, the m-value of the AMS (0.88 to 1.05) was lower than that of the posed smile (1.59 to 1.85). Conclusions: Compared to the posed smile, the AMS shows significantly larger LTRW and BCW, with significantly higher reproducibility. The AMS might serve as an adjunctive reference, in addition to the posed smile, in orthodontic and other dentomaxillofacial treatments.

• The Journal of Advanced Prosthodontics
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• v.15 no.1
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• pp.1-10
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• 2023

PURPOSE. Accuracy of image matching between resting and smiling facial models is affected by the stability of the reference surfaces. This study aimed to investigate the morphometric variations in subdivided facial units during resting, posed and spontaneous smiling. MATERIALS AND METHODS. The posed and spontaneous smiling faces of 33 adults were digitized and registered to the resting faces. The morphological changes of subdivided facial units at the forehead (upper and lower central, upper and lower lateral, and temple), nasal (dorsum, tip, lateral wall, and alar lobules), and chin (central and lateral) regions were assessed by measuring the 3D mesh deviations between the smiling and resting facial models. The one-way analysis of variance, Duncan post hoc tests, and Student's t-test were used to determine the differences among the groups (α = .05). RESULTS. The smallest morphometric changes were observed at the upper and central forehead and nasal dorsum; meanwhile, the largest deviation was found at the nasal alar lobules in both the posed and spontaneous smiles (P < .001). The spontaneous smile generally resulted in larger facial unit changes than the posed smile, and significant difference was observed at the alar lobules, central chin, and lateral chin units (P < .001). CONCLUSION. The upper and central forehead and nasal dorsum are reliable areas for image matching between resting and smiling 3D facial images. The central chin area can be considered an additional reference area for posed smiles; however, special cautions should be taken when selecting this area as references for spontaneous smiles.

• Science of Emotion and Sensibility
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• v.16 no.1
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• pp.95-106
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• 2013

The study compares accuracies between humans and computer algorithms in the discrimination of spontaneous smiles from posed smiles. For this purpose, subjects performed two tasks, one was judgment with single pictures and the other was judgment with pair comparison. At the task of judgment with single pictures, in which pictures of smiling facial expression were presented one by one, subjects were required to judge whether smiles in the pictures were spontaneous or posed. In the task for judgment with pair comparison, in which two kinds of smiles from one person were presented simultaneously, subjects were to select spontaneous smile. To calculate the discrimination algorithm accuracy, 8 kinds of facial features were used. To calculate the discriminant function, stepwise linear discriminant analysis (SLDA) was performed by using approximately 50 % of pictures, and the rest of pictures were classified by using the calculated discriminant function. In the task of single pictures, the accuracy rate of SLDA was higher than that of humans. In the analysis of accuracy on pair comparison, the accuracy rate of SLDA was also higher than that of humans. Among the 20 subjects, none of them showed the above accuracy rate of SLDA. The facial feature contributed to SLDA effectively was angle of inner eye corner, which was the degree of the openness of the eyes. According to Ekman's FACS system, this feature corresponds to AU 6. The reason why the humans had low accuracy while classifying two kinds of smiles, it appears that they didn't use the information coming from the eyes enough.