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

Aliyu Sulaiman Mukhtar; Muhammad Aminu Ahmad; Mohammad Ibrahim; Saadatu Abdulkadir; Abubakar Mu’azu; Sulaiman Dauda; Abdullahi Diso

doi:10.33003/fjs-2025-0904-3487

Authors

Aliyu Sulaiman Mukhtar Kaduna State University
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

DOI:

https://doi.org/10.33003/fjs-2025-0904-3487

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.

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

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