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 proposed method specifically tackles the limitations of existing approaches that heavily rely on facial features, which become unreliable under partial/complete occlusion scenarios. 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. The SCCA module introduces a novel bipartite grouping mechanism that leverages hotspot detection to preserve discriminative body features when facial cues are unavailable. Comprehensive experiments demonstrate that SCGNet achieves state-of-the-art performance with minimum Mean Absolute Error (MAE) 4.01% for age estimation on IMDB-Clean (2.9% improvement over VOLO-D1) 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, maintaining 97.32% gender accuracy even with complete facial occlusion on Lagenda dataset. The proposed architecture shows 73.30% CS@5 for age prediction in cross-domain testing, demonstrating superior cross-scene adaptability compared to VOLO (69.72%) and MiVOLO (71.27%). Ablation studies confirm the individual contributions of CSC, LFGF, and SCCA modules. This research provides new insights for robust identity analysis in human-computer interaction and intelligent security applications.