Sclera-Based Biometric for Age Group Estimation Using a Hybrid ResNet-50 and Gradient Boosted Neural Network
DOI:
https://doi.org/10.63746/njtd.v22i3.3328Keywords:
Age estimation, Sclera biometrics, Gradient Boosting Neural Network, Hybrid Model, Otsu thresholding, Ensemble learningAbstract
Age estimation using biometric features, particularly sclera biometrics, has emerged as a promising alternative to facial recognition in scenarios with obscured facial features, garnering attention in the fields of computer vision and security. This study proposed a hybrid sclera-based biometric model combining ResNet-50 for feature extraction and a Gradient Boosting Neural Network (GBNN) for classification to improve age group estimation. A dataset of 2,400 sclera images from 300 subjects, categorized into four age groups (Children: 0–12, Teens: 13–19, Young Adults: 20–39, Older Adults: 40+) was utilized, sourced from Nigerian populations to ensure regional representation and preprocessed using Otsu thresholding for precise sclera segmentation. Evaluated on a 60/20/20 train-validation-test split, the model achieved 94.15% accuracy, outperforming standalone ResNet-50 (92.08%). The GBNN’s iterative refinement reduced misclassifications in challenging categories (e.g., Children) by 50%, attributed to its ability to learn residual errors and adaptively weight underrepresented samples. These results highlight the potential for deployment in biometric security systems and age-restricted content moderation, particularly in low-light environments where sclera visibility is consistent. While geographic homogeneity in the dataset and illumination-dependent on Otsu thresholding represent current limitations, this work establishes a foundation for future advancements in multimodal biometric integration and cross-population validation.
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