  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 ear recognition technology an appealing choice for
surveillance and security applications as well as related application domains.
In contrast to other biometric modalities, where large datasets captured in
uncontrolled settings are readily available, datasets of ear images are still
limited in size and mostly of laboratory-like quality. As a consequence, ear
recognition technology has not benefited yet from advances in deep learning and
convolutional neural networks (CNNs) and is still lacking behind other
modalities that experienced significant performance gains owing to deep
recognition technology. In this paper we address this problem and aim at
building a CNNbased ear recognition model. We explore different strategies
towards model training with limited amounts of training data and show that by
selecting an appropriate model architecture, using aggressive data augmentation
and selective learning on existing (pre-trained) models, we are able to learn
an effective CNN-based model using a little more than 1300 training images. The
result of our work is the first CNN-based approach to ear recognition that is
also made publicly available to the research community. With our model we are
able to improve on the rank one recognition rate of the previous
state-of-the-art by more than 25% on a challenging dataset of ear images
captured from the web (a.k.a. in the wild).
