Age estimation plays an important role in humancomputer interaction system. The lack of large number of facial images with definite age label makes age estimation algorithms inefficient. Deep label distribution learning (DLDL) which employs convolutional neural networks (CNN) and label distribution learning to learn ambiguity from ground-truth age and adjacent ages, has been proven to outperform current state-of-the-art framework. However, DLDL assumes a rough label distribution which covers all ages for any given age label. In this paper, a more practical label distribution paradigm is proposed: we limit age label distribution that only covers a reasonable number of neighboring ages. In addition, we explore different label distributions to improve the performance of the proposed learning model. We employ CNN and the improved label distribution learning to estimate age. Experimental results show that compared to the DLDL, our method is more effective for facial age recognition.

年龄估计在人机交互系统中起着重要作用。缺乏具有确定年龄标签的大量面部图像使得年龄估计算法效率低下。事实证明，采用卷积神经网络（CNN）和标签分发学习来从地面真实年龄和邻近年龄学习歧义的深度标签分发学习（DLDL）优于当前的最新框架。但是，DLDL假定标签分布很粗，涵盖了任何给定年龄标签的所有年龄。在本文中，提出了一种更实用的标签分配范式：我们限制年龄标签分配，使其仅涵盖合理数量的相邻年龄。此外，我们探索了不同的标签分布，以改善建议的学习模型的性能。我们采用CNN和改进的标签分配学习方法来估算年龄。实验结果表明，与DLDL相比，我们的方法对面部年龄识别更为有效。