Ear Alignment using Deep LearningHrovatič, Anja (Avtor) Peer, Peter (Mentor) Emeršič, Žiga (Komentor) ear biometricscomputer visiondeep learningEar biometrics identification methods have become very popular in the recent years, especially since the ear presents itself as a reliable modality for recognition with attractive qualities of universality, uniqueness, measurability and permanence. In early ear recognition research, alignment has always been used as a preprocessing step to ensure reliable and robust verification and recognition systems. However, lately the ear recognition research has mostly been oriented towards obtaining better features, omitting the alignment step completely. In our research we tackle the problem of ear alignment by employing deep learning methods. We develop a framework for automatic landmark localization on 2D ears of the "In-the-wild" Ear dataset, employing means of data augmentation to obtain a large-scale dataset with annotated landmarks that is further used to train deep learning architectures. We perform landmark fitting experiments on the ITWE and AWE datasets and obtain results superior to state-of-the-art with the use of two Stack Hourglass Network architecture. Lastly, we employ landmark-based geometric normalization technique to obtain aligned ear images of both datasets and perform recognition experiments on both unaligned and aligned data.20212021-12-10 15:55:00Magistrsko delo/naloga133721sl