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@inproceedings{KinEars,

title = {Kinship Verification from Ear Images: An Explorative Study with Deep Learning Models},

author = {Grega Dvoršak and Ankita Dwivedi and Vitomir Štruc and Peter Peer and Žiga Emeršič},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/03/Gregovi_Uhlji_Template-2.pdf},

year  = {2022},

date = {2022-04-21},

urldate = {2022-04-21},

booktitle = {International Workshop on Biometrics and Forensics (IWBF)},

pages = {1--6},

abstract = {The analysis of kin relations from visual data represents a challenging research problem with important real-world applications. However, research in this area has mostly been limited to the analysis of facial images, despite the potential of other physical (human) characteristics for this task. In this paper, we therefore study the problem of kinship verification from ear images and investigate whether salient appearance characteristics, useful for this task, can be extracted from ear data. To facilitate the study, we introduce a novel dataset, called KinEar, that contains data from 19 families with each family member having from 15 to 31 ear images. Using the KinEar data, we conduct experiments using a Siamese training setup and  5 recent deep learning backbones. The results of our experiments suggests that ear images  represent a viable alternative to other modalities for kinship verification, as 4 out of 5 considered  models reach a performance of over 60% in terms of the Area Under the Receiver Operating Characteristics (ROC-AUC). },

keywords = {biometrics, CNN, deep learning, ear, ear biometrics, kinear, kinship, kinship recognition, transformer},

pubstate = {published},

tppubtype = {inproceedings}

}