Cross-age Face Image Similarity Measurement Based on Deep Learning Algorithms

In this study, a multi-feature fusion and decoupling solution based on the RNN is proposed from a discriminative perspective. This method can address the identity and age information extraction losses in cross-age face recognition. This method not only constrains the correlation between identity and age using correlation loss but also optimizes identity feature restoration using feature decoupling. The model was trained and simulated in CACD and CACD-VS datasets. The single-task learning model stabilized after 125 iterations of training, while the multi-task learning model reached a stable and convergent state after 75 iterations. In terms of performance analysis, the DE-RNN model had the highest recognition accuracy with a mAP of 92.4%. The Human Voting model had a value of 90.2%. The mAP of the Human Average model was 81.8%, whereas the mAP of the DAL model was the lowest at 78.1%. Experiments proved that the model constructed in this study has effective recognition and application value in the cross-age face recognition scenario.

@article{Zhang2023,
  title     = {Cross-age Face Image Similarity Measurement Based on Deep Learning Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {5},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jing Zhang and Ningyu Hu},
  doi       = {10.14569/IJACSA.2023.01405123},
  url       = {https://doi.org/10.14569/IJACSA.2023.01405123}
}