Fingerprint Identification for Attendance Using Euclidean Distance and Manhattan Distance

Authors

  • Adya Zizwan Putra Universitas Prima Indonesia
  • Sallyana Yek Universitas Prima Indonesia
  • Shane Christian Kwok Universitas Prima Indonesia
  • Elovani Tarigan Universitas Prima Indonesia
  • William Frans Sego Universitas Prima Indonesia

DOI:

10.33395/sinkron.v8i4.12844

Keywords:

Euclidean Distance, Fingerprint, Fingerprint Identification, Image Pre-processing, Manhattan Distance

Abstract

Attendance is an action to confirm that someone is present at the office, school, or event. The use of attendance in an agency or company is really important as it can improve the level of discipline and productivity. However, the traditional way of doing attendance is considered less effective, less secure, and more difficult to organize. Therefore, a modern attendance system that utilizes fingerprints can be the right solution, especially because every fingerprint is unique. In this research, we focus on designing a fingerprint identification system for attendance purposes by using two distance measure methods, namely Euclidean Distance and Manhattan Distance. The dataset used in the research contains 111 fingerprint images with 90 images for training the designed fingerprint identification system and the remaining 21 images for testing the system. Each fingerprint image has undergone image pre-processing stage before being used. We compare Euclidean Distance and Manhattan Distance based on their performances in identifying fingerprint. From the test results, the fingerprint identification accuracy obtained using Euclidean Distance is 76.19%, while the accuracy obtained using Manhattan Distance is 71.43%. In general, both algorithms succeed in providing the correct identification results. This proves that Euclidean Distance and Manhattan Distance can be utilized for fingerprint identification purposes.

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References

Abu-Faraj, M., Alqadi, Z. A., Al-Ahmad, B., Aldebei, K., & Ali, B. J. A. (2022). A Novel Approach to Extract Color Image Features Using Image Thinning. Applied Mathematics and Information Sciences, 16(5), 665–672. https://doi.org/10.18576/amis/160501

Ananta. (2022). 4 Fungsi Mesin Absensi untuk Perusahaan! Retrieved June 30, 2023, from https://smartpresence.id/blog/waktu-kehadiran/4-fungsi-mesin-absensi-untuk-perusahaan

Awasthi, G., Fadewar, D. H., Siddiqui, A., & Gaikwad, B. P. (2020). Analysis of Fingerprint Recognition System Using Neural Network. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3648835

Cao, K., Nguyen, D. L., Tymoszek, C., & Jain, A. K. (2020). End-to-End Latent Fingerprint Search. IEEE Transactions on Information Forensics and Security, 15(8), 880–894. https://doi.org/10.1109/TIFS.2019.2930487

Caseneuve, G., Valova, I., LeBlanc, N., & Thibodeau, M. (2021). Chest X-Ray image preprocessing for disease classification. Procedia Computer Science, 192, 658–665. https://doi.org/10.1016/j.procs.2021.08.068

Devaraj, A., Rathan, K., Jaahnavi, S., & Indira, K. (2019). Identification of plant disease using image processing technique. Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, 749–753. https://doi.org/10.1109/ICCSP.2019.8698056

Dong, X. Y., Niu, X. Q., Zhang, Z. Y., Wei, J. S., & Xiong, H. M. (2020). Red Fluorescent Carbon Dot Powder for Accurate Latent Fingerprint Identification using an Artificial Intelligence Program. ACS Applied Materials and Interfaces, 12(26), 29549–29555. https://doi.org/10.1021/acsami.0c01972

Faisal, M., Zamzami, E. M., & Sutarman. (2020). Comparative Analysis of Inter-Centroid K-Means Performance using Euclidean Distance, Canberra Distance and Manhattan Distance. Journal of Physics: Conference Series, 1566(1). https://doi.org/10.1088/1742-6596/1566/1/012112

Gifelem, K., Mangantar, M., & Uhing, Y. (2021). Analisis Efektivitas Penerapan Model Absensi Fingerprint Dalam Meningkatkan Disiplin Kerja Aparatur Sipil Negara Pada Sekretariat Daerah Kabupaten Sorong. 900 Jurnal EMBA, 9(2), 900–906. Retrieved from https://ejournal.unsrat.ac.id/index.php/emba/article/view/38486

Hoo, S. C., & Ibrahim, H. (2019). Biometric-based attendance tracking system for education sectors: A literature survey on hardware requirements. Journal of Sensors, 2019. https://doi.org/10.1155/2019/7410478

Hoover, J. E. (2023). fingerprint. In Encyclopedia Britannica. Retrieved from https://www.britannica.com/topic/fingerprint

Khairnar, S., Thepade, S. D., & Gite, S. (2021). Effect of image binarization thresholds on breast cancer identification in mammography images using OTSU, Niblack, Burnsen, Thepade’s SBTC. Intelligent Systems with Applications, 10–11, 200046. https://doi.org/10.1016/j.iswa.2021.200046

Liu, F., Liu, G., Zhao, Q., & Shen, L. (2020). Robust and high-security fingerprint recognition system using optical coherence tomography. Neurocomputing, 402, 14–28. https://doi.org/10.1016/j.neucom.2020.03.102

Madhupriya, P., & Fairooz, S. K. (2020). Enhancement of Medical Image Fusion Using Joint Sparse Method. 02(04), 225–230. https://doi.org/10.3850/978-981-09-6200-5_59

Mercioni, M. A., & Holban, S. (2019). A survey of distance metrics in clustering data mining techniques. ACM International Conference Proceeding Series, (November), 44–47. https://doi.org/10.1145/3338472.3338490

Ngurah, G., Dhanurdhara, D., Gusti, I., Wimba, A., Dewa, I. I., Wilyadewi, A. Y., … Pariwisata, D. (2022). Pengaruh Efektivitas Penerapan Absensi Fingerprint Terhadap Kinerja Pegawai Dimediasi Disiplin Kerja. Jurnal Manajemen, Kewirausahaan Dan Pariwisata, 2(1), 46–56.

Otsu, N. (1979). Threshold Selection Method From Gray-Level Histograms. IEEE Trans Syst Man Cybern, SMC-9(1), 62–66. https://doi.org/10.1109/tsmc.1979.4310076

Pulungan, A., & Saleh, A. (2020). Perancangan Aplikasi Absensi Menggunakan QR Code Berbasis Android. Jurnal Mahasiswa Fakultas Teknik Dan Ilmu Komputer, 1(1), 1063–1074. Retrieved from http://e-journal.potensi-utama.ac.id/ojs/index.php/FTIK/article/view/945

Ratih, S. (2022). Arti Absen, Tujuan, dan Manfaat Bagi Perusahaan. Retrieved June 30, 2023, from https://kerjoo.com/blog/arti-absen/

Suwanda, R., Syahputra, Z., & Zamzami, E. M. (2020). Analysis of Euclidean Distance and Manhattan Distance in the K-Means Algorithm for Variations Number of Centroid K. Journal of Physics: Conference Series, 1566(1). https://doi.org/10.1088/1742-6596/1566/1/012058

Thant, A. A., & Aye, S. M. (2020). Euclidean, Manhattan and Minkowski Distance Methods For Clustering Algorithms. International Journal of Scientific Research in Science, Engineering and Technology, 7(3), 553–559. https://doi.org/10.32628/ijsrset2073118

Win, K. N., Li, K., Chen, J., Viger, P. F., & Li, K. (2020). Fingerprint classification and identification algorithms for criminal investigation: A survey. Future Generation Computer Systems, 110(xxxx), 758–771. https://doi.org/10.1016/j.future.2019.10.019

Zhang, T. Y., & Suen, C. Y. (1984). A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3), 236–239. https://doi.org/10.1145/357994.358023

Zhou, W., Ma, X., & Zhang, Y. (2020). Research on Image Preprocessing Algorithm and Deep Learning of Iris Recognition. Journal of Physics: Conference Series, 1621(1). https://doi.org/10.1088/1742-6596/1621/1/012008

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How to Cite

Putra, A. Z. ., Yek, S., Kwok, S. C., Tarigan, E., & Sego, W. F. (2023). Fingerprint Identification for Attendance Using Euclidean Distance and Manhattan Distance. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2345-2352. https://doi.org/10.33395/sinkron.v8i4.12844

Issue

Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023

Section

Articles

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Copyright (c) 2023 Adya Zizwan Putra, Sallyana Yek, Shane Christian Kwok, Elovani Tarigan, William Frans Sego

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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