Liu Kuan-Hsien, Liu Tsung-Jung
IEEE Trans Image Process. 2019 May 20. doi: 10.1109/TIP.2019.2916768.
Developing an automatic age estimation method towards human faces continues to possess an important role in computer vision and pattern recognition. Many studies regarding facial age estimation mainly focus on two aspects: facial aging feature extraction and classification/regression model learning. To set our work apart from existing age estimation approaches, we consider a different aspect -system structuring, which is, under a constrained condition: given a fixed feature type and a fixed learning method, how to design a framework to improve the age estimation performance based on the constraint? We propose a four-stage fusion framework for facial age estimation. This framework starts from gender recognition, and then go to the second phase, gender-specific age grouping, and followed by the third stage, age estimation within age groups, and finally ends at the fusion stage. In the experiment, three well-known benchmark datasets, MORPH-II, FG-NET, and CLAP2016, are adopted to validate the procedure. The experimental results show that the performance can be significantly improved by using our proposed framework and this framework also outperforms several state-of-the-art age estimation methods.
开发一种针对人脸的自动年龄估计方法在计算机视觉和模式识别中仍然具有重要作用。许多关于面部年龄估计的研究主要集中在两个方面：面部衰老特征提取和分类/回归模型学习。为了使我们的工作与现有的年龄估计方法有所不同，我们考虑了一个不同的方面——系统构建，即在一个受限条件下：给定固定的特征类型和固定的学习方法，如何基于该约束设计一个框架来提高年龄估计性能？我们提出了一种用于面部年龄估计的四阶段融合框架。该框架从性别识别开始，然后进入第二阶段，即按性别进行年龄分组，接着是第三阶段，在年龄组内进行年龄估计，最后在融合阶段结束。在实验中，采用了三个著名的基准数据集，即MORPH-II、FG-NET和CLAP2016，来验证该过程。实验结果表明，使用我们提出的框架可以显著提高性能，并且该框架也优于几种当前最先进的年龄估计方法。
