Portrait age recognition method based on improved ResNet and deformable convolution

ResNet-based correlation models excel in age recognition algorithms, but specific age recognition research is currently limited and often plagued by substantial errors. We introduce an enhanced portrait age recognition algorithm based on ResNet, using CORAL (consistent rank logits) rank consistent ordered regression instead of traditional classification to predict precise ages. We further improve this approach by incorporating DCN (deformable convolution), resulting in the DCNR model. DCN dynamically adjusts convolution kernels for diverse faces, improving accuracy and robustness. We tested DCN-R34 and DCN-R50 against the SOTA model, achieving better results with the same complexity. This reduces the computational load while maintaining or enhancing performance.