A Comprehensive survey on ear recognition: Databases, approaches, comparative analysis, and open challenges

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 ear shape over time make this technology a promising alternative for surveillance, authentication, and forensic applications. In recent years, significant research has been conducted in this area. Nevertheless, challenges remain that limit the commercial use of this technology. Several phases of the ear recognition system have been stud-ied in the literature, from ear detection, normalization, and feature extraction to classification. This paper reviews the most recent methods used to describe and classify biometric features of the ear. We propose a first taxonomy to group existing approaches to ear recognition, including 2D, 3D, and combined 2D and 3D methods, as well as an overview of historical advances in this field. It is well known that data and algorithms are the essential components in biometrics, particularly in-ear recognition. However, early ear recognition datasets were very limited and collected in laboratory with controlled environments. With the wider use of deep neural networks, a considerable amount of training data has become neces-sary if acceptable ear recognition performance is to be achieved. As a consequence, current ear recogni-tion datasets have increased significantly in size. This paper gives an overview of the chronological evolution of ear recognition datasets and compares the performance of conventional vs. deep learning methods on several datasets. We proposed a second taxonomy to classify the existing databases, includ-ing 2D, 3D, and video ear datasets. Finally, some open challenges and trends are debated for future research.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Automatic identity recognition from ear images is an active area of research, with potential applications in surveillance, authentication, and forensics. However, there are challenges that limit the commercial use of this technology. This paper reviews recent methods for describing and classifying biometric features of the ear, proposes a taxonomy for classifying existing approaches, and discusses the evolution of ear recognition datasets and the performance of conventional vs. deep learning methods. Future research challenges and trends are also debated.