Department of Orthodontics and Dentofacial Orthopedics, Graduate School of Dentistry, Osaka University, Suita, Osaka, Japan.
Center for Advanced Medical Engineering and Informatics, Osaka University, Suita, Osaka, Japan.
PLoS One. 2019 Jul 10;14(7):e0219451. doi: 10.1371/journal.pone.0219451. eCollection 2019.
Elderly people show a decline in the ability to decode facial expressions, but also experience age-related facial structure changes that may render their facial expressions harder to decode. However, to date there is no empirical evidence to support the latter mechanism. The objective of this study was to assess the effects of age on facial morphology at rest and during smiling, in younger (n = 100; age range, 18-32 years) and older (n = 30; age range, 55-65 years) Japanese women. Three-dimensional images of each subject's face at rest and during smiling were obtained and wire mesh fitting was performed on each image to quantify the facial surface morphology. The mean node coordinates in each facial posture were compared between the groups using t-tests. Further, the node coordinates of the fitted mesh were entered into a principal component analysis (PCA) and a multifactor analysis of variance (MANOVA) to examine the direct interactions of aging and facial postures on the 3D facial morphology. The results indicated that there were significant age-related 3D facial changes in facial expression generation and the transition from resting to smiling produced a smaller amount of soft tissue movement in the older group than in the younger group. Further, 185 surface configuration variables were extracted and the variables were used to create four discriminant functions: the age-group discrimination for each facial expression, and the facial expression discrimination for each age group. For facial expression discrimination, the older group showed 80% accuracy with 2 of 66 significant variables, whereas the younger group showed 99% accuracy with 15 of 144 significant variables. These results indicate that in both facial expressions, the facial morphology was distinctly different in the younger and older subjects, and that in the older group, the facial morphology during smiling could not be as easily discriminated from the morphology at rest as in the younger group. These results may help to explain one aspect of the communication dysfunction observed in older people.
老年人在解读面部表情的能力上会出现下降，但也会经历与年龄相关的面部结构变化，这可能使他们的面部表情更难解读。然而，迄今为止，没有实证证据支持后一种机制。本研究的目的是评估年龄对面部静止和微笑时的形态的影响，研究对象为年轻组（n=100；年龄范围，18-32 岁）和老年组（n=30；年龄范围，55-65 岁）的日本女性。获取每位受试者的面部静止和微笑时的三维图像，并对每张图像进行网格拟合，以量化面部表面形态。使用 t 检验比较两组在每个面部姿势中的平均节点坐标。此外，将拟合网格的节点坐标输入主成分分析（PCA）和多因素方差分析（MANOVA），以检验年龄和面部姿势对面部三维形态的直接交互作用。结果表明，在面部表情生成方面存在显著的与年龄相关的三维面部变化，并且从静止到微笑的转换在老年组中产生的软组织运动比年轻组少。此外，提取了 185 个表面配置变量，并使用这些变量创建了四个判别函数：每个面部表情的年龄组判别，以及每个年龄组的面部表情判别。对于面部表情判别，老年组的准确率为 80%，有 66 个显著变量中的 2 个，而年轻组的准确率为 99%，有 144 个显著变量中的 15 个。这些结果表明，在两种面部表情中，年轻组和老年组的面部形态明显不同，并且在老年组中，微笑时的面部形态不能像年轻组那样容易与静止时的形态区分开来。这些结果可能有助于解释老年人观察到的沟通功能障碍的一个方面。
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