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 strategy for cross-age facial recognition is proposed, focusing on the dual decoupling of multi-level features to optimize the extraction and processing of identity features. The method begins with multi-level 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 multi-task 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.