Technol Health Care. 2021;29(S1):497-507. doi: 10.3233/THC-218047.
Age is an essential feature of people, so the study of facial aging should have particular significance.
The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features.
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.
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.
According to the experimental results, we can conclude that texture features combined with facial landmarks are useful for age prediction.
年龄是人的一个基本特征，因此研究面部衰老应该具有特殊意义。
本研究旨在通过结合面部地标和纹理特征来提高年龄预测的性能。
我们首先测量每个纹理特征的分布。从几何角度来看，面部特征点会随年龄而变化，因此研究面部特征点至关重要。我们标注面部特征点，标记相应的特征点坐标，然后使用特征点坐标和纹理特征来预测年龄。
我们使用支持向量机回归预测方法，基于提取的纹理特征和地标来预测年龄。与面部纹理特征相比，基于面部地标预测的结果更好。这表明面部地标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。结合面部地标和纹理特征可以提高年龄预测的性能。
根据实验结果，我们可以得出结论，纹理特征与面部地标相结合对年龄预测是有用的。
