Feature representations within intermediate layers of convolutional neural networks are often overlooked by current mainstream cross-age face recognition methods,whereby the output of the final convolutional layer is solely utilized as the final feature representation.As a result,the issue of incomplete identity features in the decoupled model representation is encoun-tered.A cross-age face recognition method was proposed,wherein feature representations from intermediate layers of convolu-tional neural networks were leveraged.The method was comprised of a feature selection fusion module and an identity feature decoupling module.A hybrid feature was obtained by fusing attention-based low-level features and high-level semantics.Under the supervision of multi-task training,identity features were extracted through non-linear feature decoupling.The cross-age face recognition was achieved by employing the extracted identity features.The effectiveness of the proposed method is demonstrated through accuracies of 96.89％,96.20％,and 99.60％achieved on the AgeDB-30,CALFW,and CACD-VS face aging datasets,respectively.
维普
万方数据