Cross-Identity Interaction Transformer for Facial Age Estimation.

Despite the remarkable progress being made in the study of human facial age estimation, it is still a challenging problem. The main problem lies in the large intra-age appearance variations among different individuals. Sometimes, these variations can even exceed the inter-age appearance variations of the same individual. To address this problem, we construct a cross-identity image sequence for each query image and reformulate age estimation as a multi-image learning task. This provides a basis for learning common age-related cues across identities. Based on this formulation, we propose the Cross-Identity Interaction Transformer (CIIT) for age estimation. The CIIT first extracts multi-scale aging cues through a cross-scale embedding (CSE) module to preserve age evidence from fine textures to coarse structural changes. Secondly, to progressively enhance facial features and capture shared facial characteristics from cross-identity references, intra-image feature attention (IFA) and prior-guided axial cross-image attention (PG-ACIA) operate alternately within each Transformer block. IFA refines local age-discriminative representations within each image, while PG-ACIA uses multi-scale edge priors to guide cross-image interaction toward age-sensitive regions such as wrinkles. Finally, an anchored regression network (ARN) predicts age through a soft-weighted combination of multiple linear regressors for robust age estimation under diverse facial aging patterns. Experiments on four benchmark datasets, namely MORPH Album II, MegaAge-Asian, FG-NET and Adience, demonstrate that the proposed method achieves superior performance across multiple evaluation metrics, validating the effectiveness of the CIIT in capturing shared facial characteristics.