Age prediction based on a small number of facial landmarks and texture features.
Age prediction based on a small number of facial landmarks and texture features.
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基于少量面部标志和纹理特征的年龄预测
DOI：
10.3233/thc-218047
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Chen W
中科院分区：
文献类型：
--
作者：
Wang M;Chen W
背景：年龄是人的一个基本特征，因此面部衰老的研究具有特殊的意义。目的：本研究的目的是通过结合面部标志和纹理特征来提高年龄预测的性能。方法：我们首先测量每个纹理特征的分布。从几何学的角度看，人脸特征点会随着年龄的增长而发生变化，因此对人脸特征点的研究是必不可少的。我们对人脸特征点进行标注，标注相应的特征点坐标，然后利用特征点坐标和纹理特征来预测年龄。结果：基于提取的纹理特征和界标，采用支持向量机回归预测方法进行年龄预测。与人脸纹理特征相比，基于人脸特征点的预测效果更好。这表明，面部标志中包含的面部形态特征比面部纹理特征更能反映面部年龄。结合人脸特征点和纹理特征，可以提高年龄预测的性能。结论：根据实验结果，我们可以得出结论，纹理特征结合面部标志是有用的年龄预测。
BACKGROUND: Age is an essential feature of people, so the study of facial aging should have particular significance. OBJECTIVE: The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features. METHODS: We first measure the distribution of each texture feature. From a geometric point of view, facial feature points will change with age, so it is essential to study facial feature points. We annotate the facial feature points, label the corresponding feature point coordinates, and then use the coordinates of feature points and texture features to predict the age. RESULTS: We use the Support Vector Machine regression prediction method to predict the age based on the extracted texture features and landmarks. Compared with facial texture features, the prediction results based on facial landmarks are better. This suggests that the facial morphological features contained in facial landmarks can reflect facial age better than facial texture features. Combined with facial landmarks and texture features, the performance of age prediction can be improved. CONCLUSIONS: According to the experimental results, we can conclude that texture features combined with facial landmarks are useful for age prediction.