Zhao Wei, Wang Han
School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
Sensors (Basel). 2016 Jun 28;16(7):994. doi: 10.3390/s16070994.
Nowadays, label distribution learning is among the state-of-the-art methodologies in facial age estimation. It takes the age of each facial image instance as a label distribution with a series of age labels rather than the single chronological age label that is commonly used. However, this methodology is deficient in its simple decision-making criterion: the final predicted age is only selected at the one with maximum description degree. In many cases, different age labels may have very similar description degrees. Consequently, blindly deciding the estimated age by virtue of the highest description degree would miss or neglect other valuable age labels that may contribute a lot to the final predicted age. In this paper, we propose a strategic decision-making label distribution learning algorithm (SDM-LDL) with a series of strategies specialized for different types of age label distribution. Experimental results from the most popular aging face database, FG-NET, show the superiority and validity of all the proposed strategic decision-making learning algorithms over the existing label distribution learning and other single-label learning algorithms for facial age estimation. The inner properties of SDM-LDL are further explored with more advantages.
如今，标签分布学习是面部年龄估计领域最先进的方法之一。它将每个面部图像实例的年龄视为具有一系列年龄标签的标签分布，而非通常使用的单一实际年龄标签。然而，这种方法在其简单的决策标准方面存在缺陷：最终预测年龄仅在描述度最高的那个标签处被选中。在许多情况下，不同的年龄标签可能具有非常相似的描述度。因此，仅仅依据最高描述度盲目确定估计年龄会遗漏或忽视其他可能对最终预测年龄有很大贡献的有价值的年龄标签。在本文中，我们提出了一种战略决策标签分布学习算法（SDM-LDL），它具有一系列针对不同类型年龄标签分布的策略。来自最流行的衰老面部数据库FG-NET的实验结果表明，所有提出的战略决策学习算法相对于现有的标签分布学习算法以及其他用于面部年龄估计的单标签学习算法具有优越性和有效性。SDM-LDL的内在特性得到了进一步探索，且具有更多优势。
