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Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints

Kruthi R1 , Abhijit Patil2 , Shivanand Gornale3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 22-29, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.2229

Online published on Jan 31, 2019

Copyright © Kruthi R, Abhijit Patil, Shivanand Gornale . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Kruthi R, Abhijit Patil, Shivanand Gornale, “Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.22-29, 2019.

MLA Style Citation: Kruthi R, Abhijit Patil, Shivanand Gornale "Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints." International Journal of Computer Sciences and Engineering 7.1 (2019): 22-29.

APA Style Citation: Kruthi R, Abhijit Patil, Shivanand Gornale, (2019). Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints. International Journal of Computer Sciences and Engineering, 7(1), 22-29.

BibTex Style Citation:
@article{R_2019,
author = {Kruthi R, Abhijit Patil, Shivanand Gornale},
title = {Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {22-29},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3456},
doi = {https://doi.org/10.26438/ijcse/v7i1.2229}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.2229}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3456
TI - Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints
T2 - International Journal of Computer Sciences and Engineering
AU - Kruthi R, Abhijit Patil, Shivanand Gornale
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 22-29
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

Gender identification of an individual is a fundamental task, as many social interactions are gender-based. The fingerprint is the most precise and reliable biometric trait for gender identification. It plays a vital role to link the suspect in a crime scene or to find an unknown person. The gender identification can significantly enhance the performance of authentication systems and reduces the search space and speed up the matching rate. Several previous studies have investigated the gender identification from fingerprints but lack’s in conventional results. In this work, the authors propose gender identification based on fingerprints by using the fusion of two well-known local descriptors, such as LBP and LPQ. The proposed algorithm is evaluated on state of two datasets i.e. publically available SDUMLA-HMT fingerprint dataset and other is self-created fingerprint dataset, which embraces fingerprints of 348 individuals (10 samples from each individual) of which 183 are males and 165 are female volunteers and obtained the best classification rate of 97% accuracy using SVM classifier. The results are competitive and appreciable as compared to earlier methods.

Key-Words / Index Term

Gender Identification, Biometrics, Fingerprint, LBP, LPQ, KNN, and SVM

References

[1] Anil K. Jain, Karthik Nandakumar, Xiaoguang Lu, and Unsang park, “Integrating Faces, Fingerprints, and Soft Biometric Traits for user Recognition.” Proceedings of Biometric Authentication Workshop, LNCS 3087, pp.259-269, PRAGUE,-May 2004.
[2] Suchita T, Akhile Anjikar and Hemant Thakur, ”Fingerprint-Based Gender Identification using DWT Transformation”, International Conference on Computing Communication Control and Automation,(ICCCA-2015), Pune , India,2015.
[3] Gnanasivam .P, and Dr. Muttan S, “Fingerprint Gender Classification Using Wavelet Transform and Singular Value Decomposition”, International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012.
[4] Akanchha Gour and Dharmendra Roy,” Increasing Accuracy of Age and Gender Detection by Fingerprint Analysis Using DCT “, International Journal of Innovative Research in Computer and Communication Engineering, Vol 9, Issue 5, May 2016.
[5] S. F. Abdullah, A. F. N. A. Rahman, Z. A. Abas and W. H. M. Saad,” Multilayer Perceptron Neural Network in Classifying Gender using Fingerprint Global Level Features“, Indian Journal of Science and Technology, Vol 9, March 2016.
[6] A. S. Falohun, O.D.Fenwa, F. A. Alala,” A Fingerprint-based Age and Gender Detector System using Fingerprint Pattern Analysis ”, International Journal of Computer Applications (0975 – 8887) Vol.4, pp.136 –140, February 2016.
[7] M vadivel , T Arulkumaran “Gender Identification from FingerPrint Images Based on a Supervised Learning Approach”, PASJ International Journal of Computer Science (IIJCS), Volume 2, Issue 7, July 2014.
[8] Pragya Bharti, Dr. C. S. Lamba,” DWT-Neural Network based Gender Classification”, International Journal of Digital Application & Contemporary research, Volume 2, Issue 8, March 2014.
[9] Vikas Humbe, S S Gornale , K V Kale, R. R. Manza’, “Mathematical Morphology Approach for Genuine Fingerprint Feature Extraction”, International Journal of Computer Science and Security, ISSN: 1985-1533 Vol. 1 Issue 2, pp 53-59-2007.
[10] Manish Verma and Suneeta Agarwal.’’ Fingerprint Based Male - Female Classification. ’’ in Proceedings of the international workshop on computational intelligence in security for information systems (CISIS’08), Genoa, Italy, pp.251 – 255, 2008.
[11] A. Badawi, M. Mahfouz, R. Tadross, and R. Jantz “Fingerprint-based gender classification” The International Conference on Image Processing, Computer Vision, and Pattern Recognition,(CVPR-2006) in June 2006.
[12] Naveen Kumar Jain, Sunil Sharma, Anurag Paliwal., A Real-Time Approach To Determine The Gender Using Fingerprints”, IJAIR ISSN: 2278-7844, pp:229-233, 2012.
[13] Rijo Jackson Tom, T. Arulkumaran, “Fingerprint-Based Gender Classification Using 2D Discrete Wavelet Transforms and Principal Component Analysis”, International Journal of Engineering Trends and Technology, Vo. 4, Issue 2,2013
[14] Pallavi Chand, Shubhendu Kumar Sarangi, “A Novel Method for Gender Classification Using DWT and SVD Techniques”, International Journal of Computer Technology & Applications, Vol 4 (3),pp.445-449, May-June 2013.
[15] Ritu Kaur and Susmita Ghosh Mazumdar, Mr. Devanand Bhonsle, “A Study On Various Methods of Gender Identification Based on Fingerprints”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Vol.2, Issue 4, April 2012.
[16] S. S. Gornale, Mallikarjun Hangarge, Rajmohan Pardeshi, Kruthi R“ Haralick Feature Descriptors for Gender Classification Using Fingerprints: A Machine Learning Approach “,IJARCSSE Vol. 5,Issue 9,September-2015.
[17] S. S. Gornale, Geetha D, Kruthi R “Analysis of a fingerprint image for gender classification using spatial and frequency-domain analysis”, American International Journal of Research in Science, Technology, Engineering and Mathematics”,ISSN 2328-3491, ISSN:2328-3580, ISSN (CD-ROM): 2328-3629, pp.46-50, 2013.
[18] S. S. Gornale, “Fingerprint-Based Gender Classification for Biometric Security: A State-Of-The-Art Technique”,International Journal of Research in Science, Technology, Engineering & Mathematics ISSN 2328-3491, pp. 39-49 Dec-2014.
[19] V. Ojansivu and J. Heikkilä, “Blur insensitive texture classification using local phase quantization,” in Image and Signal Processing. Heidelberg: Springer, 2008, pp. 236-243.
[20] Shivanand Gornale, Abhijit Patil and Veersheety C. “Fingerprint-Based Gender Identification Using DWT and Gabor Filters”, International Journal of Computer Applications Vol.152 Issue 4, pp.34-37.2016
[21] Shivanand Gornale, Basavana M, and Kruti R, “Fingerprint-Based Gender Classification Using Local Binary Pattern”, International Journal of Computational Intelligence and Research Vol.13 Issue 2, pp. 261-272,2017.
[22] Prabha,S Jitendra, and P Rajmohan,”Fingerprint-based Automatic Human Gender Identification”, International Journal of Computer Applications ISSN 0975 – 8887, Vol-170 Issue-7, July 2017 .
[23] Otsu, Nobuyuki. "A threshold selection method from gray-level histograms", IEEE Transactions on systems, man, and cybernetics Vol.9 ,Issue 1,pp.62-66. 1979
[24] Timo Ojala, Matti Pietikainen, and David Harwood, “A comparative study of texture measures with classification based on feature distributions”, Pattern Recognition Vol.29 Issue 1, pp.51-59, 1996.
[25] Yilong Yin, Lili Liu, and Xiwei Sun,” SDUMLA-HMT: A Multimodal Biometric Database”, The 6th Chinese Conference on Biometric Recognition (CCBR 2011), LNCS 7098, pp. 260-268, Beijing, China, 2011.