基于标签分布构造的异构数据集年龄估算

Age Estimation for Isomerism Data Sets Based on Label Distribution Construction

现有年龄估算方法的性能度量主要是基于训练集与测试集独立同分布的假设。为了能更好地符合实际场景以及更好地评估年龄估算方法的泛化性能,提出一种异构数据集评估协议,即在年龄估算时更关注训练集与测试集具有的不同分布和特征情况。此外,为了提高基于卷积神经网络的年龄估算方法的拟合能力,在充分考虑相邻年龄特性的基础上,通过将年龄估算问题建模为基于高斯模型的标签分布学习,提出一种新颖的损失函数。理论分析与实验结果皆说明本文方法的有效性与鲁棒性。

Existing age estimation methods of performance measurement are mainly based on the training set and testing set of independent identically distributed hypothesis.In order to better conform to the actual scene and better assess the age estimation method of generalization performance,a kind of heterogeneous data sets to evaluate agreement is put forward,i.e.paying more attention to the training set and test set with different distribution and characteristics.In addition,in order to improve the age estimation method based on convolution neural network fitting ability,on the basis of fully considering the adjacent age characteristics,a new theory of loss function analysis is proposed through modeling the age estimation problem as the label distribution study based on Gauss model.Theoretical analysis and experimental results show the effectiveness and robustness of the proposed method.