School of Computer Science and Engineering, Southeast University, Jiangning, Nanjing, Jiangsu, China.
IEEE Trans Pattern Anal Mach Intell. 2013 Oct;35(10):2401-12. doi: 10.1109/TPAMI.2013.51.
One of the main difficulties in facial age estimation is that the learning algorithms cannot expect sufficient and complete training data. Fortunately, the faces at close ages look quite similar since aging is a slow and smooth process. Inspired by this observation, instead of considering each face image as an instance with one label (age), this paper regards each face image as an instance associated with a label distribution. The label distribution covers a certain number of class labels, representing the degree that each label describes the instance. Through this way, one face image can contribute to not only the learning of its chronological age, but also the learning of its adjacent ages. Two algorithms, named IIS-LLD and CPNN, are proposed to learn from such label distributions. Experimental results on two aging face databases show remarkable advantages of the proposed label distribution learning algorithms over the compared single-label learning algorithms, either specially designed for age estimation or for general purpose.
面部年龄估计的主要困难之一是学习算法无法期望获得足够和完整的训练数据。幸运的是，由于衰老过程是缓慢而平稳的，所以近距离的人脸看起来非常相似。受此观察结果的启发，本文不是将每个面部图像视为具有一个标签（年龄）的实例，而是将每个面部图像视为与标签分布相关联的实例。标签分布涵盖了一定数量的类别标签，表示每个标签描述实例的程度。通过这种方式，一个面部图像不仅可以有助于学习其实际年龄，还可以有助于学习其相邻年龄。本文提出了两种算法，即 IIS-LLD 和 CPNN，用于从这种标签分布中进行学习。在两个老化人脸数据库上的实验结果表明，与专门为年龄估计或通用目的而设计的比较单标签学习算法相比，所提出的标签分布学习算法具有显著的优势。
