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.Comment: Accepted by CVPR2023; 8 pages, 3 figure