Ear Biometric System using GLCM Algorithm

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Author(s)

Sajal Kumar Goel 1,* Mrudula Meduri 1

1. Computer Science Engineering in SRM University, Chennai – 603 203, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2017.10.07

Received: 3 Jun. 2017 / Revised: 10 Jul. 2017 / Accepted: 27 Jul. 2017 / Published: 8 Oct. 2017

Index Terms

Biometrics, Ear, Pre-processing, GLCM, Identification, Verification

Abstract

Biometric verification is a mean by which a person can be uniquely authenticated by evaluating some distinguishing biological traits. Fingerprinting is the ancient and the most widely used biometric authentication system today which is succeeded by other identifiers such as hand geometry, earlobe geometry, retina and iris patterns, voice waves and signature. Out of these verification methods, earlobe geometry proved to be most efficient and reliable option to be used either along with existing security system or alone for one level of security. However in previous work, the pre-processing was done manually and algorithms have not necessarily handled problems caused by hair and earing. In this paper, we present a more systematic, coherent and methodical way for ear identification using GLCM algorithm which has overcome the limitations of other successful algorithms like ICP and PCA. GLCM elucidates the texture of an image through a matrix formed by considering the number of occurrences of two pixels which are horizontally adjacent to each other in row and column. Pre-processing techniques and algorithms will be discussed and a step-by-step procedure to implement the system will be stated.

Cite This Paper

Sajal Kumar Goel, Mrudula Meduri, "Ear Biometric System using GLCM Algorithm", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.10, pp.68-76, 2017. DOI:10.5815/ijitcs.2017.10.07

Reference

[1]V. K. Narendira Kumar and B. Srinivasan, “Ear Biometrics in Human Identification System”, International Journal of Information Technology and Computer Science, 2012, 2, 41-47.

[2]H. Chen and B. Bhanu. Contour matching for 3D ear recognition. In Seventh IEEE Workshop on Application of Computer Vision, pages 123–128, 2005.

[3]R. F. Walker, P. T. Jackway, I. D. Longstaff, “Recent Developments in the use of co-occurrence Matrix for Texture Recognition”, in Proc. 13th international Conference on Digital Signal Processing, Brisbane – Queensland University, 1997.

[4]B. Bhanu and H. Chen, “Human Ear Recognition in 3D,” Proc. Workshop Multimodal User Authentication, pp. 91-98, 2003.

[5]Prakash Chandra Srivastava, Anupam Agarwal, Kamta Nath Mishra, P. K. Ojha, R. Garg, “Fingerprints, Iris and DNA features based on Multimodal System: A Review”, International Journal of Information Technology and Computer Science, vol. 5, pp. 88-111, 2013.   

[6]K. Chang, K. Bowyer, and V. Barnabas, “Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, pp. 1160-1165, 2003.

[7]Madhusmita Sahoo. “Biomedical Image Fusion and Segmentation using GLCM”, in. Proc. International Journal of Computer Application Special Issue on “2nd National Conference- Computing, Communication and Sensor Network CCS, pp: 34 – 39, 2011.

[8]H. B. Kekre, D. T. Sudeep, K. S. Tanuja, S. V. Suryawanshi,” Image Retrieval using Texture Features extracted from GLCM, LBG and KPE” , International Journal of Computer Theory and Engineering., Vol. 2, pp: 1793-8201, 2010.

[9]C. W. D. de Almeida, R.M. C. R. de Souza, A. L. B. Candeias, “ Texture classification based on co-occurrence matrix and self – organizing map”, IEEE International conference on Systems Man& Cybernetics, University of pernambuco, Recife, 2010.

[10]R. M. Haralick, S Shanmugam, I. Dinstein, “Textural features for image classification”, IEEE Transactions on Systems, Man and Cybernetics. SMC., Vol. 3, pp: 610- 621, 1973.

[11]C.Nageswara Rao, S.Sreehari Sastry, K.Mallika, Ha Sie Tiong, K.B.Mahalakshmi, “Co-Occurrence Matrix and Its Statistical Features as an Approach for Identification Of Phase Transitions Of Mesogens”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, Issue 9, September 2013.

[12]Dhanashree Gadkari, “Image Quality Analysis Using Glcm”, pages 08-09, 2000.

[13]Angel Johnsy (2011) BlogSpot [ONLINE]. Available:http://angeljohnsy.blogspot.com/2011/04/matlab-code-histogram-equalization.html

[14]Daniel Kim, “Sobel Operator and Canny Edge Detector”, pages 05-06, Fall 2013.

[15]Snehlata Barde, A S Zadgaonkar, G R Sinha,” PCA based Multimodal Biometrics using Ear and Face Modalities”, International Journal of Information Technology and Computer Science, vol. 6, pp. 43-49, 2014.