# Deep Ear Biometrics for Gender Classification

arXiv: https://arxiv.org/abs/2308.08797 · HTML: https://mlsanity.com/paper/2308.08797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Ear Recognition: More Than a Survey](https://mlsanity.com/paper/1611.06203/) (2016)
- [Multimodal Age and Gender Classification Using Ear and Profile Face Images](https://mlsanity.com/paper/1907.10081/) (2019)
- [Age and Gender Classification From Ear Images](https://mlsanity.com/paper/1806.05742/) (2018)

## Abstract (arXiv)

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.