Abstract
Ear recognition has become a vital issue in image processing to identification and analysis for many geometric applications. This article reviews the source of ear modelling, details the algorithms, methods and processing steps and finally tracks the error and limitations for the input database for the final results obtain for ear identification. The commonly used machine-learning techniques used were Naïve Bayes, Decision Tree and K-Nearest Neighbor, which then compared to the classification technique of Deep Learning using Convolution Neural Networks. The results achieved in this article by the Deep Learning using Convolution Neural Network was 92.00% average ear identification rate for both left and right ear.
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Booysens, A., Viriri, S. (2020). Exploration of Ear Biometrics with Deep Learning. In: Chmielewski, L.J., Kozera, R., Orłowski, A. (eds) Computer Vision and Graphics. ICCVG 2020. Lecture Notes in Computer Science(), vol 12334. Springer, Cham. https://doi.org/10.1007/978-3-030-59006-2_3
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DOI: https://doi.org/10.1007/978-3-030-59006-2_3
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