Deep Ear Biometrics for Gender Classification

TL;DR

This paper introduces a deep CNN model for automatic gender classification using ear images, achieving high accuracy with less computational complexity, demonstrating the ear's potential as a reliable biometric trait.

Contribution

The study develops a novel deep CNN approach for gender classification from ear images, utilizing pre-trained models with reduced computational requirements.

Findings

Achieved 93% accuracy on EarVN1.0 dataset.

Reduced computational complexity compared to existing methods.

Validated ear biometrics as effective for gender classification.

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.