改进的CloFormer模型与有序回归相结合的年龄评估方法

Age estimation method combining improved CloFormer model and ordinal regression

现有的年龄评估方法通常采用基于卷积神经网络(CNN)的有序回归,然而在预测相邻年龄时,CNN难以捕获全局特征表示,进而导致预测精度的下降。为了解决该问题,提出一种新的将改进的CloFormer模型与有序回归相结合的年龄评估方法。相较于传统的基于CNN的有序回归,CloFormer在捕捉图像特征时能够利用自注意力机制更好地捕捉图像中不同区域之间的关系,从而更好地学习相邻年龄之间的特征差异。首先,优化CloFormer模型;然后,将优化后的CloFormer模型与有序回归相结合,以便更好地利用年龄序列信息,实现更精准的年龄预测;接着,通过端到端优化训练改进后的CloFormer模型和有序回归模型,更好地学习面部特征和年龄序列的关系;最后,在多个公开数据集上对比实验。实验结果表明,所提方法在CACD、AFAD、UTKFace数据集上的均方根误差(RMSE)分别为7.36、4.62、8.28,与基于CNN的有序回归(OR-CNN)、秩一致性有序回归模型(CORAL)等现有年龄评估方法相比,在CACD数据集上分别减小了0.25、0.05,在AFAD数据集上分别减小了0.18、0.03,在UTKFace数据集上分别减小了0.97、0.53,可见所提方法取得了较好的年龄评估结果。

Existing methods for age estimation typically employ ordinal regression based on Convolutional Neural Network(CNN).However,when predicting adjacent ages,CNN is difficult in capturing global feature representations,resulting in a decrease in prediction accuracy.In order to solve the problem,an age estimation method was proposed,which combined an enhanced CloFormer model with ordinal regression.Compared to traditional CNN-based ordinal regression,CloFormer,when capturing image features,can better utilize self-attention mechanism to capture relationships between different regions in an image,thereby improving the learning of feature differences between adjacent ages.In the proposed method,firstly,the CloFormer model was optimized,and then the optimized CloFormer model was combined with ordinal regression to better utilize the age sequence information,achieving more precise age estimation.Subsequently,through endto-end optimization training of the improved CloFormer model and ordinal regression model,the proposed method was able to better learn the relationships between facial features and age sequences.Finally,comparative experiments were conducted on multiple publicly available datasets.Experimental results show that on CACD,AFAD,and UTKFace datasets,the Root Mean Square Error(RMSE)of the proposed method is 7.36,4.62,and 8.28,respectively.In comparison to existing age estimation methods such as Ordinal Regression with CNN(OR-CNN)and COnsistent RAnk Logits(CORAL),the RMSEs are reduced by 0.25 and 0.05 respectively on CACD dataset,0.18 and 0.03 respectively on AFAD dataset,and 0.97 and 0.53 respectively on UTKFace dataset,illustrating that the proposed method has better age estimation results.