Abstract: Over the recent years, there has been an immense attraction towards age detection due to its raised implementation in various sectors. Such as government regulations and rules, security control, and human computer interaction. Popular human features such as the face and fingerprints can be modified or changed with time. However, ear has a stable structure that does not change with time and have unique features that satisfies the requirements of a biometric trait. This research presents a detailed analysis extracting the features of the human ear only by applying Deep Learning techniques. In particular, Convolutional Neural Network (CNN) is applied on large datasets which have multiple layers to extract the features and classify them. The proposed methodology increased the number of the dataset by collecting more private children datasets, and consequently achieved high accuracy by 98.75% along with amending the architecture of the selected neural network compared to previous studies. This research can be benefited to control the contents of social media by detecting the age of group whether it is under 18 or above 18.

Manal Alghieth, Jawaher Alhuthail, Kholod Aldhubiay and Rotan Alshowaye, “Smart Age Detection for Social Media” International Journal of Advanced Computer Science and Applications(IJACSA), 10(11), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101143

@article{Alghieth2019,
title = {Smart Age Detection for Social Media},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2019.0101143},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101143},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {11},
author = {Manal Alghieth and Jawaher Alhuthail and Kholod Aldhubiay and Rotan Alshowaye},
}