Spatial correlation guided cross scale feature fusion for age and gender estimation
Shiyi Jiang1, Qing Ji1, Hukui Shi1
1Guizhou Mobile Information Technology Co., Ltd, Guiyang, 550002, China.
Scientific Reports
|July 2, 2025
Summary
This study introduces SCGNet for accurate age and gender recognition, even with severe facial occlusion. The novel network effectively uses body features, achieving state-of-the-art results in challenging, uncontrolled scenarios.
Area of Science:
- Computer Vision
- Machine Learning
- Biometrics
Background:
- Facial recognition systems struggle with occlusion and uncontrolled environments.
- Existing methods fail when facial features are absent or obscured.
Purpose of the Study:
- To develop a robust network for age and gender recognition despite severe facial occlusion.
- To overcome the limitations of facial-feature-dependent recognition systems.
Main Methods:
- Proposed Spatial Correlation Guided Cross Scale Feature Fusion Network (SCGNet).
- Integrated Cross-Scale Combination (CSC) and Local Feature Guided Fusion (LFGF) modules.
- Introduced Spatial Correlation Composition Analysis (SCCA) using Getis-Ord Gi* statistics for feature reorganization and hotspot detection.
Main Results:
- Achieved state-of-the-art age estimation (MAE 4.01%) and gender classification accuracy on multiple datasets.
- Maintained high gender accuracy (97.32%) even with complete facial occlusion.
- Demonstrated superior cross-scene adaptability in age prediction (73.30% CS@5).
Conclusions:
- SCGNet effectively utilizes non-facial features for robust identity analysis.
- Spatial correlation modeling enhances recognition accuracy in occluded scenarios.
- The method offers significant improvements for human-computer interaction and intelligent security.
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