Human recognition with biometrics is a rapidly emerging area of computer vision. Compared
to other well-known biometric features such as the face, fingerprint, iris, and palmprint, the …

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 …

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 …

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 …

User authentication is an important issue on the Internet and usually solved through static
and often unique passwords. Another method is to use biometrics, but biometric data are …

Recently, several deep learning schemes have been considered for recognizing ear print.
However, deep-based schemes are known to be data-hungry and they may require a …

Ear detection represents one of the key components of contemporary ear recognition
systems. While significant progress has been made in the area of ear detection over recent …

In this paper, a new method for image mosaicing (image stitching) is introduced based on
Scale Invariant Feature transform (SIFT). One of the main drawbacks of SIFT is the …

Ear biometrics is a fast-developing field of computer vision that offers several advantages
over existing biometric authentication methods. The performance of the ear is superior to or …

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 …