  Biometric-based personal authentication systems have seen a strong demand
mainly due to the increasing concern in various privacy and security
applications. Although the use of each biometric trait is problem dependent,
the human ear has been found to have enough discriminating characteristics to
allow its use as a strong biometric measure. To locate an ear in a 2D side face
image is a challenging task, numerous existing approaches have achieved
significant performance, but the majority of studies are based on the
constrained environment. However, ear biometrics possess a great level of
difficulties in the unconstrained environment, where pose, scale, occlusion,
illuminations, background clutter etc. varies to a great extent. To address the
problem of ear localization in the wild, we have proposed two high-performance
region of interest (ROI) segmentation models UESegNet-1 and UESegNet-2, which
are fundamentally based on deep convolutional neural networks and primarily
uses contextual information to localize ear in the unconstrained environment.
Additionally, we have applied state-of-the-art deep learning models viz; FRCNN
(Faster Region Proposal Network) and SSD (Single Shot MultiBox Detecor) for ear
localization task. To test the model's generalization, they are evaluated on
six different benchmark datasets viz; IITD, IITK, USTB-DB3, UND-E, UND-J2 and
UBEAR, all of which contain challenging images. The performance of the models
is compared on the basis of object detection performance measure parameters
such as IOU (Intersection Over Union), Accuracy, Precision, Recall, and
F1-Score. It has been observed that the proposed models UESegNet-1 and
UESegNet-2 outperformed the FRCNN and SSD at higher values of IOUs i.e. an
accuracy of 100\% is achieved at IOU 0.5 on majority of the databases.
