Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric
procedures have been predominantly used to ensure identity and ear recognition …

Identity recognition from ear images is an active field of research within the biometric
community. The ability to capture ear images from a distance and in a covert manner makes …

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 …

Automated personal identification using the shape of the human ear is emerging as an
appealing modality in biometric and forensic domains. This is mainly due to the fact that the …

Recently, deep convolutional neural networks (CNNs) have been used for ear recognition
with the increasing and available ear image databases. However, most known ear …

The morphology of the human ear presents rich and stable information embedded on the
curved 3-D surface and has as a result attracted considerable attention from forensic …

The use of the ear as a biometric modality has emerged in recent years. It makes it possible
to differentiate people thanks to its stability over time and to the richness of its characteristics …

Ear recognition problem is known as selecting whether two ear images belong to the same
person or not, this consider as a challenge due to variation in lighting, background, pose …

In this paper, a framework is proposed to super-resolve low resolution ear images and to
recognise these images, without external dataset. This frame uses linear kernel co-variance …

Recently biometric systems have shown improved capabilities because of the remarkable
success of deep learning in solving various computer vision tasks. In ear recognition, the …