Dual-Decoupling and Multi-Level Feature Integration for Cross-Age Face Recognition

Efficient decoupling of rich facial features is crucial in the realm of cross-age face recognition. A novel strategy for cross-age facial recognition is proposed, focusing on the dual decoupling of multilevel features to optimize the extraction and processing of identity-related features. The method begins with multilevel feature extraction on facial images through convolutional neural networks, acquiring a series of low-dimensional and high-dimensional hybrid features, which are then effectively integrated. Subsequently, these fused features are introduced into both linear and nonlinear decomposition units. Under the supervision of multitask training, features related to individual identities are decoupled. Finally, the extracted identity features are utilized to perform cross-age facial recognition tasks. When evaluated on multiple standard cross-age facial recognition datasets and standard universal facial recognition datasets, the method demonstrates high accuracy, highlighting its significant advantages in effectiveness and generalizability.