ENHANCING AGE ESTIMATION FROM SCLERA IMAGES USING RESNET-50, VGG16, AND RANDOM FOREST

Authors

  • Aliyu Sulaiman Mukhtar
    Kaduna State University image/svg+xml
  • Muhammad Aminu Ahmad
    Kaduna State University, Kaduna
  • Mohammad Ibrahim
    Kaduna State University, Kaduna
  • Saadatu Abdulkadir
    Kaduna State University, Kaduna
  • Abubakar Mu’azu
    Kaduna State University, Kaduna
  • Sulaiman Dauda
    Kaduna State University, Kaduna
  • Abdullahi Diso
    Kaduna State University, Kaduna

Keywords:

Deep Learning, Deep Neural Network, Convolutional Neural Network, VGG16, Resnet 50, Random Forest, machine learning

Abstract

This study presents a novel hybrid model for age prediction from sclera images, combining deep learning architectures ResNet-50 and VGG-16 with a Random Forest classifier. The hybrid approach aims to optimize both accuracy and computational efficiency, addressing limitations in previous methodologies. Results demonstrate exceptional performance, with the hybrid model achieving an overall accuracy of 98.85% and outperforming benchmark models. Detailed evaluation metrics reveal high precision, recall, and F1-scores across age groups, supported by insights from the confusion matrix. The model's practical applicability is demonstrated through efficient training and testing processes. This research bridges gaps in existing literature by integrating transfer learning, deep learning, and ensemble methods, while also addressing issues of computational complexity. The study underscores the potential of hybrid models to advance age prediction from biometric images, setting a new benchmark for future research in the field.

Dimensions

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Published

30-04-2025

How to Cite

ENHANCING AGE ESTIMATION FROM SCLERA IMAGES USING RESNET-50, VGG16, AND RANDOM FOREST. (2025). FUDMA JOURNAL OF SCIENCES, 9(4), 252-261. https://doi.org/10.33003/fjs-2025-0904-3487

How to Cite

ENHANCING AGE ESTIMATION FROM SCLERA IMAGES USING RESNET-50, VGG16, AND RANDOM FOREST. (2025). FUDMA JOURNAL OF SCIENCES, 9(4), 252-261. https://doi.org/10.33003/fjs-2025-0904-3487

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