A Cross-Age Face Recognition Method Utilizing Non-linear Decoupling of Multi-level Features

This study introduces a approach to cross-age facial recognition, highlighting the crucial role of extracting rich hybrid features and isolating identity characteristics within them. Our proposed method integrates a Feature Aggregation and Selection Module with a Non-linear Identity Feature Separation module. The process begins with the generation of hybrid features through an attention-driven fusion of basic and advanced semantic features. This is followed by the extraction of identity features via Non-linear decoupling, guided by multi-task training. These identity features are then applied to cross-age facial recognition tasks. The effectiveness and adaptability of our approach are demonstrated by its impressive performance on various datasets, including AgeDB-30, CALFW, CACD-VS, and LFW, achieving accuracy rates of 97.10%, 96.22%, 99.61%, and 99.71% respectively. This method represents a significant advancement in the field of facial recognition research, particularly in addressing the challenges of age variation.