Automatic identity recognition from ear images represents an active field of research within
the biometric community. The ability to capture ear images from a distance and in a covert …

Automatic identity recognition from ear images is an active research topic in the biometric
community. The ability to secretly acquire images of the ear remotely and the stability of the …

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 possibility of identifying people by the shape of their outer ear was first discovered by
the French criminologist Bertillon, and refined by the American police officer Iannarelli, who …

Identity recognition using local features extracted from ear images has recently attracted a
great deal of attention in the intelligent biometric systems community. The rich and reliable …

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 …

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