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@incollection{ZigaBook2019,

title = {Deep ear recognition pipeline},

author = {Emersic Ziga and Krizaj Janez and Struc Vitomir and Peer Peter},

editor = {Hassaballah Mahmoud and Hosny Khalid M.},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/Emeršič2019_Chapter_DeepEarRecognitionPipeline_submitted.pdf},

doi = {10.1007/978-3-030-03000-1_14},

isbn = {1860-9503},

year  = {2019},

date = {2019-01-01},

booktitle = {Recent advances in computer vision : theories and applications},

volume = {804},

publisher = {Springer},

abstract = {Ear recognition has seen multiple improvements in recent years and still remains very active today. However, it has been approached from recognition and detection perspective separately. Furthermore, deep-learning-based approaches that are popular in other domains have seen limited use in ear recognition and even more so in ear detection. Moreover, to obtain a usable recognition system a unified pipeline is needed. The input in such system should be plain images of subjects and the output identities based only on ear biometrics. We conduct separate analysis through detection and identification experiments on the challenging dataset and, using the best approaches, present a novel, unified pipeline. The pipeline is based on convolutional neural networks (CNN) and presents, to the best of our knowledge, the first CNN-based ear recognition pipeline. The pipeline incorporates both, the detection of ears on arbitrary images of people, as well as recognition on these segmented ear regions. The experiments show that the presented system is a state-of-the-art system and, thus, a good foundation for future real-word ear recognition systems.},

keywords = {ear, ear recognition, pipeline},

pubstate = {published},

tppubtype = {incollection}

}