In the past few years, deep learning-based models have been very successful in achieving
state-of-the-art results in many tasks in computer vision, speech recognition, and natural …

The recognition performance of visual recognition systems is highly dependent on extracting
and representing the discriminative characteristics of image data. Convolutional neural …

This paper employs state-of-the-art Deep Convolutional Neural Networks (CNNs), namely
AlexNet, VGGNet, Inception, ResNet and ResNeXt in a first experimental study of ear …

This paper presents ear recognition models constructed with Deep Residual Networks
(ResNet) of various depths. Due to relatively limited amounts of ear images we propose …

The authors present an unconstrained ear recognition framework that outperforms state‐of‐
the‐art systems in different publicly available image databases. To this end, they developed …

The authors perform unconstrained ear recognition using transfer learning with deep neural
networks (DNNs). First, they show how existing DNNs can be used as a feature extractor …

Ear recognition is an active research area in the biometrics community with the ultimate goal
to recognize individuals effectively from ear images. Traditional ear recognition methods …

In this chapter, we address the problem of biometric identity recognition from the vasculature
of the human sclera. Specifically, we focus on the challenging task of multi-view sclera …

Here, the authors have extensively investigated the unconstrained ear recognition problem.
The authors have first shown the importance of domain adaptation, when deep …

Getting to an ear recognition model that can overcome all challenges and difficulties was
and still the main objective of researchers for years. One particular problem we highlight …