综合年龄估计机制 (Integrated Age Estimation Mechanism)

Machine-learning-based age estimation has received lots of attention. Traditional age estimation mechanism focuses estimation age error, but ignores that there is a deviation between the estimated age and real age due to disease. Pathological age estimation mechanism the author proposed before introduces age deviation to solve the above problem and improves classification capability of the estimated age significantly. However,it does not consider the age estimation error of the normal control (NC) group and results in a larger error between the estimated age and real age of NC group. Therefore, an integrated age estimation mechanism based on Decision-Level fusion of error and deviation orientation model is proposed to solve the problem.Firstly, the traditional age estimation and pathological age estimation mechanisms are weighted together.Secondly, their optimal weights are obtained by minimizing mean absolute error (MAE) between the estimated age and real age of normal people. In the experimental section, several representative age-related datasets are used for verification of the proposed method. The results show that the proposed age estimation mechanism achieves a good tradeoff effect of age estimation. It not only improves the classification ability of the estimated age, but also reduces the age estimation error of the NC group. In general, the proposed age estimation mechanism is effective. Additionally, the mechanism is a framework mechanism that can be used to construct different specific age estimation algorithms, contributing to relevant research.

翻译：传统的年龄估计机制注重估计年龄错误,但忽视了估计年龄和实际年龄因疾病而出现差异。作者在引入年龄偏差之前提议的病理年龄估计机制,目的是解决上述问题,并大大提高估计年龄的分类能力。然而,它并不考虑正常控制(NC)组别的年龄估计错误,导致NC组别估计年龄和实际年龄之间的更大误差。因此,提议基于决定级别错误和偏差方向模型的综合年龄估计机制,以解决问题。第一,传统年龄估计和病理年龄估计机制是同时加权的。第二,通过尽量减少估计年龄和实际年龄之间的平均绝对误差(MAE)来取得最佳加权。在实验部分,使用若干具有代表性的年龄数据集来核实拟议方法。结果显示,拟议的年龄估计机制在年龄估计方面产生了良好的权衡效应。不仅提高了估计年龄的分类能力,而且还减少了年龄估计的病理学年龄机制。第二,采用不同的年龄估计机制,为计算具体年龄机制提供了有效的估计。