An Intelligent Human Age and Gender Forecasting Framework Using Deep Learning Algorithms

ABSTRACT

Dental images are utilized to gather significant signs that are useful in disease diagnosis, treatment, and forensic examination. Many dental age and gender detection procedures have limitations, such as minimal accuracy and dependability. Gender identification techniques aren’t well studied, despite the fact that classification effectiveness and accuracy are low. The suggested approach takes into account the shortcomings of the current system. Deep learning techniques can successfully resolve issues that occurred in other classifiers. Human gender and age identification is a crucial process in the fields of forensics, anthropology, and bio archeology. The image preparation and feature extraction process are accomplished by deep learning algorithms. The performance of classification is improved by minimizing the occurrence of loss with the assistance of a spike neuron-based convolutional neural network (SN-CNN). The performance of SN-CNN is examined by comparing the performance metrics with the existing state-of-art techniques. SN-CNN-based classifier achieved 99.6% accuracy over existing techniques.

Acknowledgments

Research Supporting Project number (RSP-2021/323), King Saud University, Riyadh, Saudi Arabia.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This project is funded by King Saud University, Riyadh, Saudi Arabia.