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 this paper, we propose an unsupervised lightweight network with a single layer for ear
print recognition. We refer to this method by MDFNet because it relies on gradient …

Distance metric learning aims to deal with the data distribution by learning a suitable
distance metric from the training instances. For distance metric learning, the optimization …

As one of the most important biometrics, ear biometrics is getting more and more attention.
Ear recognition has unique advantages and can make identification more secure and …

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

Metric learning has significantly improved machine learning applications such as face re-
identification and image classification using K-Nearest Neighbor (KNN) and Support Vector …

Ear images are easy to capture, and ear features are relatively stable and can be used for
identification. The ear images are all asymmetric, and the asymmetry of the ear images …

In recent years, biometric recognition patterns have attracted the attention of many
researchers, among which human ears, as a unique and stable biometric feature, have …

Distance metric learning (DML) has achieved great success in many real-world applications.
However, most existing DML models characterize the quality of tuples on the tuple level …

Recently, the ear recognition system takes more increasingly interesting for many
applications, especially, in immigration system, forensic, and surveillance applications. For …