DAA: A Delta Age AdaIN operation for age estimation via binary code transformer
This work addresses age estimation in computer vision, which is incremental as it builds on transfer learning and AdaIN techniques.
The paper tackles the problem of facial age estimation by introducing a Delta Age AdaIN (DAA) operation that uses binary code mapping to compare input features with learned age-specific style maps, achieving better performance with fewer parameters compared to state-of-the-art methods on multiple datasets.
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.