一致性保留的集成排序年龄估计方法

Consistency preserving age estimation method by ensemble ranking

在基于传统的排序、回归的年龄估计方法中,存在不能有效利用人脸的演化特征、构建不同排序标签之间的相关性,且二分类方法进行年龄估计会产生排序不一致的问题。基于上述问题,提出一致性保留的集成排序年龄估计方法,充分利用年龄与排序值之间的相关性,抑制排序不一致问题;并提出新指标——排序不一致样本比例,用于评估二分类排序方法中排序不一致问题。首先,通过设计的编码方法将年龄类别转换成排序矩阵形式;然后,使用残差网络ResNet34(Residual Network)特征提取网络提取面部特征,再通过提出的编码学习模块进行编码学习;最后,通过基于度量方法的排序解码器将网络预测结果解码成图片的预测年龄。在MORPHⅡ数据集上的实验结果达到平均绝对误差(MAE)2.18,并在其他公开数据集上与同样基于排序、有序回归方法的OR-CNN(Ordinal Regression with CNN)、CORAL(COnsistent RAnk Logits)等方法相比,所提方法有更准确的预测结果,同时抑制了排序不一致样本的比例,排序不一致度量性能比OR-CNN方法提升了约65%。

The traditional age estimation methods based on ranking and regression cannot effectively utilize the evolutionary characteristics of human faces and build correlation between different ranking labels.Moreover,using binary classification methods for age estimation may result in inconsistent ranking issues.To solve above problems,an age estimation method based on integrated ranking matrix encoding and consistency preserving was proposed to fully utilize the correlation between age and ranking value and suppress the problem of inconsistent ranking.A new indicator,the proportion of samples with inconsistent ranking,was proposed to evaluate the problem of inconsistent rankings in the two-class ranking method.First,age categories were converted into a ranking matrix form through a designed coding method.Then,the ResNet34(Residual Network)feature extraction network was used to extract facial features,which were then learned through the proposed encoding learning module.Finally,the network prediction results were decoded into the predicted age of the image through a ranking decoder based on a metric method.The experimental results show that:the proposed method achieves a Mean Absolute Error(MAE)of 2.18 on MORPHⅡdataset,and has better results on other publicly available datasets compared to methods also based on ranking and ordinal regression,such as OR-CNN(Ordinal Regression with CNN)and CORAL(COnsistent RAnk Logits);at the same time,the proposed method decreases the proportion of samples with inconsistent ranking,and improves the measurement performance of ranking inconsistency by about 65%compared to the OR-CNN method.