Abstract
Ear has many unique features which can be used for uniquely identifying an individual. Ear as a biometric is very effective and efficient as the medical studies have shown that the significant changes in the shape of the ear happen only before the age of 8 years and after the age of 70 years. The ear is fully grown till the age of 8 years and after that it grows symmetrically by 1.22 mm per year. Also, ear starts to bulge downwards after the age of 70 years. The skin colour distribution of the ear is almost uniform. Ear biometric system can capture the ear from a distance even without the knowledge of the subject under test as it is a passive biometric system. Ear is hard to replicate which will be helpful to reduce cybercrime. Digital cameras capture profile face of the subject at different angles and orientations, from which ear is segmented and further using Gabor filter features are extracted which is fed to a machine learning model to train our data. As Gabor features are extracted from ear images at different angles and different orientations, the system is invariant to rotation of profile face in same or different planes.
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All the participants are hereby expressing their approval for usage of photographs therein and we are not under any influence. We take the responsibility involved in the publication of our paper. Authors have taken permission to conduct the experiments.
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Valechha, L., Valecha, H., Ahuja, V., Chawla, T., Sengupta, S. (2020). Orisyncrasy—An Ear Biometrics on the Fly Using Gabor Filter. In: Jain, V., Chaudhary, G., Taplamacioglu, M., Agarwal, M. (eds) Advances in Data Sciences, Security and Applications. Lecture Notes in Electrical Engineering, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-15-0372-6_37
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