Naked eye recognition of age is usually based on comparison with the age of others. However, this idea is ignored by computer tasks because it is difficult to obtain representative contrast images of each age. Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the style map of each age through the learned values representing the mean and standard deviation. We let the input of transfer learning as the binary code of age natural number to obtain continuous age feature information. The learned two groups of values in Binary code mapping are corresponding to the mean and standard deviation of the comparison ages. In summary, our method consists of four parts: FaceEncoder, DAA operation, Binary code mapping, and AgeDecoder modules. After getting the delta age via AgeDecoder, we take the average value of all comparison ages and delta ages as the predicted age. Compared with state-of-the-art methods, our method achieves better performance with fewer parameters on multiple facial age datasets.

翻译：人眼对年龄的识别通常基于与他人的年龄比较。然而，这一思路在计算机任务中被忽视，原因在于难以获取每个年龄的代表性对比图像。受迁移学习启发，我们设计了Delta Age AdaIN（DAA）操作来获取与各年龄的特征差异，该操作通过学习的均值和标准差表征值获得各年龄风格图。我们将年龄自然数的二进制编码作为迁移学习输入，以获取连续年龄特征信息。二进制编码映射中学习到的两组值分别对应对比年龄的均值和标准差。综上所述，本方法包含四个模块：人脸编码器（FaceEncoder）、DAA操作、二进制编码映射和年龄解码器（AgeDecoder）。通过AgeDecoder获得年龄差值后，取所有对比年龄与年龄差值的平均值作为预测年龄。与现有最优方法相比，本方法在多个面部年龄数据集上以更少的参数取得了更优性能。