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Fadhillah, Muhammad Aulia Nur
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Leveraging Convolutional Neural Networks Preprocessing for Accurate Age and Gender Classification in Personalized Nutrition Fadhillah, Muhammad Aulia Nur; Al Hakim, Shidiq; Rodiah, Rodiah; Data, Mahendra; Siagian, Al Hafiz Akbar Maulana; Riyanto, Slamet; Madenda, Sarifuddin; Apriani, Niken Fitria; Maukar, Maukar
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101923

Abstract

Imagine having a personalized nutrition plan that caters to your unique dietary needs based on your age and gender. Such a system could revolutionize the way we approach health and wellness. A key component of this vision is the accurate classification of age and gender from facial images, which can be leveraged to provide tailored nutritional recommendations. In this paper, we explored the use of convolutional neural networks and their preprocessing techniques to enhance the performance of age and gender classification models. Our age classification aimed to identify the age group according to the regulation of the Indonesian Republic's Health Ministry in 2014 about the guidelines for balanced nutrition, which includes the following categories: 10-12 years old, 13-15 years old, 16-18 years old, 19-29 years old, and 30-49 years old. We utilized the ResNet50 and Inception-v3 models, which were fine-tuned on the UTKFace dataset, a collection of more than 20,000 face images with corresponding age and gender labels. However, the UTKFace dataset suffers from a data imbalance problem. To address this issue, we proposed innovative data augmentation methods to create a more balanced dataset. Our experimental results demonstrated that our proposed augmented methods could significantly improve the classification performances of the models, leading to more accurate age and gender predictions. This advancement in facial attribute classification could pave the way for developing personalized nutrition systems that cater to individual needs and preferences, ultimately improving health outcomes and quality of life.
Co-Authors Al Hakim, Shidiq Apriani, Niken Fitria Mahendra Data Maukar Maukar Rodiah Rodiah Sarifuddin Madenda SIAGIAN, AL HAFIZ AKBAR MAULANA Slamet Riyanto