SCGNet: Spatial Correlation Guided Cross Scale Feature Fusion for Age and Gender Estimation

To address the challenges of age and gender recognition in uncontrolled scenarios with facial absence or severe occlusion, this paper proposes a Spatial Correlation Guided Cross Scale Feature Fusion Network (SCGNet). The method integrates multi-granularity semantic features through a Cross-Scale Combination (CSC) module, enhances local detail representation using a Local Feature Guided Fusion (LFGF) module, and designs a Spatial Correlation Composition Analysis (SCCA) module based on Getis-Ord Gi* statistics for feature reorganization, effectively resolving interference from non-informative regions. Experimental results demonstrate that SCGNet achieves state-of-the-art performance with minimum Mean Absolute Error (MAE) for age estimation and highest gender classification accuracy on IMDB-Clean, UTKFace, and Lagenda datasets, showing improvements in cross-scene adaptability compared to VOLO and MiVOLO models respectively. Notably, the method maintains gender discrimination accuracy under complete facial occlusion scenarios, validating the effectiveness of spatial correlation modeling for non-facial feature reasoning. This research provides new insights for robust identity analysis in human-computer interaction and intelligent security applications.