Deep Ear Biometrics for Gender Classification

• URL: http://arxiv.org/abs/2308.08797v1
• Date: Thu, 17 Aug 2023 06:15:52 GMT
• Title: Deep Ear Biometrics for Gender Classification
• Authors: Ritwiz Singh, Keshav Kashyap, Rajesh Mukherjee, Asish Bera, and Mamata Dalui Chakraborty
• Abstract summary: We have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset.
• Score: 3.285531771049763
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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