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首页> 外文会议>Federated Conference on Computer Science and Information Systems >Machine-learning at the service of plastic surgery: a case study evaluating facial attractiveness and emotions using R language
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Machine-learning at the service of plastic surgery: a case study evaluating facial attractiveness and emotions using R language

机译：整形外科服务的机器学习：使用R语言评估面部吸引力和情绪的案例研究

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Since the plastic surgery should consider that facial impression is always dependent on current facial emotion, it came to be verified how precise classification of facial images into sets of defined facial emotions is.Multivariate regression was performed using R language to identify indicators increasing facial attractiveness after undergoing rhinoplasty. Bayesian naive classifiers, decision trees (CART) and neural networks, respectively, were applied to assign a landmarked facial image data into one of the facial emotions, based on Ekman-Friesen FACS scale.Enlargement of nasolabial and nasofrontal angle within rhinoplasty significantly predicts facial attractiveness increasing (p<0.05). Decision trees showed the geometry of a mouth, then eyebrows and finally eyes affect in this descending order an impact on classified emotion. Neural networks proved the highest accuracy of the classification.Performed machine-learning analyses pointed out which geometric facial features increase facial attractiveness the most and should be consequently treated by plastic surgeries.
机译：由于整容手术应考虑面部印象始终取决于当前的面部表情，因此已被证实如何将面部图像精确地分类为定义的面部表情集。使用R语言进行多元回归以识别增加面部吸引力的指标。接受隆鼻手术。基于Ekman-Friesen FACS量表，分别使用贝叶斯朴素分类器，决策树（CART）和神经网络将具有里程碑意义的面部图像数据分配给面部表情之一。吸引力增加（p ＜0.05）。决策树显示的是嘴巴的几何形状，然后是眉毛，最后是眼睛，以这种降序顺序影响分类情感。神经网络证明了分类的最高准确性。进行的机器学习分析指出，哪些几何面部特征可以最大程度地增加面部吸引力，因此应通过整形外科对其进行治疗。

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