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

DAA: A Delta Age AdaIN operation for age estimation via binary code transformer