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Human Ear Recognition Methods Based on Image Rotation

Suharjito Suharjito(1*), Alpha Epsilon(2), Abba Suganda Girsang(3)

(1) Computer Science Department, Binus Graduate Program, Bina Nusantara University, Indonesia
(2) Computer Science Department, Binus Graduate Program, Bina Nusantara University, Indonesia
(3) Computer Science Department, Binus Graduate Program, Bina Nusantara University, Indonesia
(*) Corresponding author


DOI: https://doi.org/10.15866/ireaco.v10i5.12299

Abstract


The ear is part of biometrics that has a unique and stable structure, avoided from aging. This study aims to look at the accuracy of Geometric Moment invariant (GMI) and Zernike Moment invariant (ZMI) with Self-Organizing Maps to recognize human ears. This study used data from ear AMI database, consisting of 125 ears from 25 different people, in which each person has 5 images of the right ear. In this study, each image of the ear is modified with a different rotation angle. The accuracy of ear image recognition using GMI is 75.20% while using ZMI is 66%. The accuracy of using Geometric Moment Invariant is higher by 9.2% because Geometric Moment Invariant does not change the result of the image to be recognized due to translation, scaling, reflection and rotation.GMI cannot recognize the image of the ear with an average of 24.80% while the ZMI cannot recognize the image of the ear with an average of 34.40%. From the test results, at a rotation angle of 30 ° CCW, the geometric moment method has the best accuracy, while the Zernike moment method has the best accuracy at 30 ° CW. The best angle recognized by these two methods is 30°.
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Keywords


Ear Recognition; Geometric Moment Invariant; Zernike Moment Invariant; Self-Organizing Maps

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