• 제목/요약/키워드: Posed smile

검색결과 3건 처리시간 0.019초

Use of autonomous maximal smile to evaluate dental and gingival exposure

  • Wang, Shuai;Lin, Hengzhe;Yang, Yan;Zhao, Xin;Mei, Li;Zheng, Wei;Li, Yu;Zhao, Zhihe
    • 대한치과교정학회지
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    • 제48권3호
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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.

Three-dimensional morphometric analysis of facial units in virtual smiling facial images with different smile expressions

  • Hang-Nga Mai;Thaw Thaw Win;Minh Son Tong;Cheong-Hee Lee;Kyu-Bok Lee;So-Yeun Kim;Hyun-Woo Lee;Du-Hyeong Lee
    • The Journal of Advanced Prosthodontics
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    • 제15권1호
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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.

자발적 웃음과 인위적 웃음 간의 구분: 사람 대 컴퓨터 (Discrimination between spontaneous and posed smile: Humans versus computers)

  • 엄진섭;오형석;박미숙;손진훈
    • 감성과학
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    • 제16권1호
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    • pp.95-106
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    • 2013
  • 본 연구에서는 자발적인 웃음과 인위적인 웃음을 변별하는 데 있어서 일반 사람들의 정확도와 컴퓨터를 이용한 분류 알고리즘의 정확도를 비교하였다. 실험참가자들은 단일 영상 판단 과제와 쌍비교 판단과제를 수행하였다. 단일 영상판단 과제는 웃음 영상을 한 장씩 제시하면서 이 영상의 웃음이 자발적인 것인지 인위적인 것인지를 판단하는 것이었으며, 쌍비교 판단과제는 동일한 사람에게서 얻은 두 종류의 웃음 영상을 동시에 제시하면서 자발적인 웃음 영상이 어떤 것인지 판단하는 것이었다. 분류 알고리즘의 정확도를 산출하기 위하여 웃음 영상 각각에서 8 종류의 얼굴 특성치들을 추출하였다. 약 50%의 영상을 사용하여 단계적 선형판별분석을 수행하였으며, 여기서 산출된 판별함수를 이용하여 나머지 영상을 분류하였다. 단일 영상에 대한 판단결과, 단계적 선형판별분석의 정확도가 사람들의 정확도보다 높았다. 쌍비교에 대한 판단결과도 단계적 선형판별분석의 정확도가 사람들의 정확도보다 높았다. 20명의 실험참가자 중 선형판별분석의 정확도를 넘어서는 사람은 없었다. 판별분석에 중요하게 사용된 얼굴 특성치는 눈머리의 각도로, 눈을 가늘게 뜬 정도를 나타낸다. Ekman의 FACS에 따르면, 이 특성치는 AU 6에 해당한다. 사람들의 정확도가 낮은 이유는 두 종류의 웃음을 구별할 때, 눈에 관한 정보를 충분히 사용하지 않았기 때문으로 추론되었다.

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