Deep Ear Biometrics for Gender Classification
Ritwiz Singh Keshav Kashyap Rajesh Mukherjee Asish Bera Mamata Dalui Chakraborty
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.