Influence of alignment on ear recognition

Metod Ribič (2016) Influence of alignment on ear recognition. EngD thesis.

Preview

PDF Download (5Mb)

Abstract

Ear as a biometric modality presents a viable source for automatic human recognition especially in surveillance scenarios where face is not seen frontally. In recent years local description methods have been gaining on popularity due to their invariance to illumination and occlusion. However, these methods require that images are well aligned and preprocessed as good as possible. This causes one of the greatest challenges of ear recognition: sensitivity to pose variations. In this paper we test the influence of alignment on recognition performance on images from recently presented Annotated Web Ears dataset with alignment methods Random sample consensus (RANSAC) and Cascaded Pose Regression (CPR). We prove that alignment improves recognition rate but only on images with small angle on roll and yaw axis. On other pictures RANSAC and CPR fails to align ears and recognition rate is therefore lower versus unaligned pictures. Those pictures should be addressed with more advanced alignment methods in order to improve recognition rate.

Actions (login required)