Deep Ear Biometrics for Gender Classification
This work addresses gender classification for computer vision applications, but it is incremental as it applies existing deep learning methods to a specific biometric dataset.
The paper tackled gender classification from ear images using a deep convolutional neural network, achieving 93% accuracy on the EarVN1.0 dataset with reduced computational complexity.
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.