Ear Recognition Based on Fusion of Ear and Tragus Under Different Challenges

Abstract

This paper proposes a 2D ear recognition approach that is based on the fusion of ear and tragus using score-level fusion strategy. An attempt to overcome the effect of partial occlusion, pose variation and weak illumination challenges is done since the accuracy of ear recognition may be reduced if one or more of these challenges are available. In this study, the effect of the aforementioned challenges is estimated separately, and many samples of ear that are affected by two different challenges concurrently are also considered. The tragus is used as a biometric trait because it is often free from occlusion; it also provides discriminative features even in different poses and illuminations. The features are extracted using local binary patterns and the evaluation has been done on three datasets of USTB database. It has been observed that the fusion of ear and tragus can improve the recognition performance compared to the unimodal systems. Experimental results show that the proposed method enhances the recognition rates by fusion of parts that are nonoccluded with tragus in the cases of partial occlusion, pose variation and weak illumination. It is observed that the proposed method performs better than feature-level fusion methods and most of the state-of-the-art ear recognition systems.

On the use of ear and profile faces for distinguishing identical twins and nontwins

“…Ear recognition is a popular research area in recent years. Several studies use ear biometrics for person identification (Alqaralleh & Toygar, ; Hurley, Nixon, & Carter, ; Yan & Bowyer, , ). One of the most popular approaches for two‐dimensional ear recognition that treats ear image as a point in multidimensional space is principal component analysis (PCA; Yan & Bowyer, , ), which uses a set of orthogonal basis vectors that describe the major variation among training images.…”