Open Access Open Access  Restricted Access Subscription or Fee Access

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



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°.
Copyright © 2017 Praise Worthy Prize - All rights reserved.


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

Full Text:



M. Choraś, "Ear biometrics based on geometrical feature extraction. Progress in Computer Vision and Image Analysis,," p. 321, 2005.

J. B. Jawale and A. S. Bhalchandra, "The Human Identification System Using Multiple Geometrical," International Journal of Emerging Technology and Anvanced Engineering, vol. 2, no. 3, pp. 662 - 666, 2012.

T. Dunstone and N. Yager, Biometric System and Data Analysis: Design, Evaluation, and Data Mining. Springer, ., Eveleigh, NSW 1430: Springer, 2009.

J. Taliba, "Moment Based Extraction on Handwritten Digits. Doctor Philosophy. University Technology Malaysia, Skudai.," 2005.

S. A. Daramola and O. D. Oluwaninyo, "Automatic Ear Recognition System using Back Propagation Neural Network," International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS, vol. 11, no. 01, pp. 28-32, 2011.

B. S. EL-Desouky, M. El-Kady, M. Z. Rashad and M. M. Eid, "Ear Recognition and Occlusion," International Journal of Computer Science & Information Technology (IJCSIT), vol. 4, no. 6, pp. 97 - 104, 2012.

A. Hussein and A. Al-Timemy, "A Robust Algorithm for Ear Recognition System Based on Self Organization Maps. The 1st Regional Conference of Eng. Sci. NUCEJ.," vol. 11, pp. 315-321, 2008.

Y. Xu and W. Zeng , "Ear Recognition Based on Centroid and Spindle," Procedia Engineering, vol. 29, no. 1, pp. 2162 - 2166, 2012.

H. M. El-Bakry and N. Mastorakis, "Ear Recognition by using Neural Networks," Mathematical Methods and Applied Computing, pp. 770-804, 2005.

N. Jamil, A. Almisreb and A. A. Halin, "Illumination - Invariant Ear Authentication," Procedia Computer Science, vol. 42, pp. 271 - 278, 2014.

A. S. Anwar, K. K. A.Ghany and H. Elmahdy, "Human Ear Recognition Using Geometrical Features Extraction," Procedia Computer Science, vol. 65, pp. 529 - 537, 2015.

Z. Huang and J. Leng, "Analysis of Hu’s Moment Invariants on Image Scalling and Rotation," in Conference on Computer Engineering and Technology, China, 2012.

D. Shailaja and P. Gupta, "A Simple Geometric Approach for Ear Recognition," in 9th International Conference on Information Technology (ICIT'06), 2006.

R. Mukundan and K. R. Ramakrishnan, Moment Functions in Image Analysis Theory and Applications, Singapore: World Scientific Publishing Co Pte. Ltd, 1998.

Y. Bin and P. Jia-xiong, "Improvement and Invariance Analysis of Zernike Moments using as a Region-based Shape Descriptor," in Proceeding XV Brazilian Symposim on Computer Graphics and Image Processing, IEEE, 2002.

N. A. Bakar and S. M. Shamsuddin, "United Zernike Invariants for Character Images," pp. 498 - 509, 2009.

A. Gionis, H. Mannila and P. Tsaparas, "Clustering Aggregation," ACM Transaction on Knowledge Discovery from data, vol. 1, no. 1, pp. 1-30, 2007.

S. M. Guthikonda, "Kohonen Self-Organizing Maps," Wittenberg University, 2005.

H. Yin, "The Self Organizing Maps: Background, Theories, Extentions and Applications," Studies in Computational Intelligence (SCI), vol. 115, pp. 715 - 762, 2008.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2020 Praise Worthy Prize