Deep Ear Biometrics for Gender Classification
Computer Vision and Pattern Recognition
2023-08-21 v1
Abstract
Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances, and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset.
Cite
@article{arxiv.2308.08797,
title = {Deep Ear Biometrics for Gender Classification},
author = {Ritwiz Singh and Keshav Kashyap and Rajesh Mukherjee and Asish Bera and Mamata Dalui Chakraborty},
journal= {arXiv preprint arXiv:2308.08797},
year = {2023}
}
Comments
10 pages, 4 figures, 2 tables