Version 1
: Received: 3 January 2024 / Approved: 4 January 2024 / Online: 4 January 2024 (07:41:39 CET)
How to cite:
George, G.; Uppin, C.; Bello, U.A. Human Age Estimation from Face Images with Deep Convolutional Neural Networks Using Transfer Learning. Preprints2024, 2024010350. https://doi.org/10.20944/preprints202401.0350.v1
George, G.; Uppin, C.; Bello, U.A. Human Age Estimation from Face Images with Deep Convolutional Neural Networks Using Transfer Learning. Preprints 2024, 2024010350. https://doi.org/10.20944/preprints202401.0350.v1
George, G.; Uppin, C.; Bello, U.A. Human Age Estimation from Face Images with Deep Convolutional Neural Networks Using Transfer Learning. Preprints2024, 2024010350. https://doi.org/10.20944/preprints202401.0350.v1
APA Style
George, G., Uppin, C., & Bello, U.A. (2024). Human Age Estimation from Face Images with Deep Convolutional Neural Networks Using Transfer Learning. Preprints. https://doi.org/10.20944/preprints202401.0350.v1
Chicago/Turabian Style
George, G., Chandrashekhar Uppin and Usman Abubakar Bello. 2024 "Human Age Estimation from Face Images with Deep Convolutional Neural Networks Using Transfer Learning" Preprints. https://doi.org/10.20944/preprints202401.0350.v1
Abstract
In recent years, there has been a growing interest in the prediction of facial age due to its diverse applications in fields such as security, entertainment, and healthcare. The current scourge of underaged voting in Nigeria is a problem and this research delves into the realm of facial age prediction by employing four well-known convolutional neural network (CNN) architectures, namely VGG16, ResNet50, Mobile Net, and VGG19 to predict chronological ages from facial pictures of person. The objective is to achieve precise age estimation from facial images, utilizing two datasets: UTKFace and CASIA African Facial Datasets. The results of this investigation are noteworthy. Specifically, the VGG16 model which demonstrated remarkable performance, yielding a Mean Absolute Error (MAE) of 1.76 when applied to the UTK-Face dataset. Additionally, when utilizing Mobile-Net, an unprecedented MAE of 1.10 was achieved for the Casia Africa Face Dataset. Notably, this marks the first instance of employing the dataset for facial age detection with CNNs, and this approach outperformed previous works, yielding the lowest MAE among all the studies reviewed.
Keywords
Convolution neural network; Deep Learning; Facial Age; Regression; Transfer learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
