Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB
Abstract
needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.
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S. N. Fatimah T, “Pencantuman Status Perkawinan Dalam Administrasi Perkawinan di Kantor Urusan Agama Perspektif Maqashid Syari’ah”, AD, vol. 23, no. 1, pp. 79 - 92, Apr. 2020. https://doi.org/10.35719/aladalah.v23i1.28
Djamhari, Eka A., et al. “Kondisi Kesejahteraan Lansia dan Perlindungan Sosial Lansia di Indonesia.” Perkumpulan PRAKARSA, 2021.
Putera, Roni E., and Tengku R. Valentina. "Implementasi Program KTP Elektronik (E-KTP) di Daerah Percontohan." Mimbar: Jurnal Sosial dan Pembangunan, vol. 27, no. 1, pp. 193-201. 2011 https://doi.org/10.29313/mimbar.v27i2.328
Fatmasari, Fatmasari, et al. "Evaluasi Penerimaan Sistem E-ktp dengan Menggunakan Tam (Technology Acceptance Model) (Studi Kasus: Kantor Camat Ilir Timur I Palembang)." Seminar Nasional Informatika 2013, Yogyakarta, Indonesia, 2013. Universitas Pembangunan Nasional "Veteran". 2013. http://jurnal.upnyk.ac.id/index.php/semnasif/article/view/845
Tawa’A, Nunung, and Andi Y. Katili. "Kemampuan Pegawai dalam Penginputan Data E-ktp di Kantor Camat Tibawa Kabupaten Gorontalo." Publik, vol. 6, no. 1, pp. 16-22. 2019 https://doi.org/10.37606/publik.v6i1.20.
Iswati, Heni, and Eny Retnoningrum. "Mengukur Layanan Website E-Govqual terhadap Kepuasan Masyarakat dalam Mengakses Rekap E-KTP." Serasi, vol. 17, no. 2, pp. 101-110. 2019. https://journal.budiluhur.ac.id/index.php/serasi/article/view/949
Akbar, Kevin Adhiguna, Firhan Maulana Rusli, and Hendy Irawan. “Building an ID Card Repository with Progressive Web Application to Mitigate Fraud”. ArXiv. December 2020. https://arxiv.org/abs/2012.08295
Vilàs Mari, P. Classification of Identity Documents Using a Deep Convolutional Neural Network. Master’s Thesis, Universitat Oberta de Catalunya, Barcelona (Spain), 2018. Available online: http://hdl.handle.net/10609/73186 (accessed on December 4, 2020).
Ren, S., He, K., Girshick, R.B., & Sun, J. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149. 2015
https://doi.org/10.1109/TPAMI.2016.2577031
Rusli, Firhan Maulana, Kevin Adhiguna Akbar, and Hendy Irawan. “Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing”. ArXiv, 2020.
https://arxiv.org/abs/2101.05214
Pratama, M. & Satyawan, Wira & Fajar, Bagus & Fikri, Rusnandi & Hamzah, Haris. "Indonesian ID Card Recognition using Convolutional Neural Networks". 2018. Proceeding of the Electrical Engineering Computer Science and Informatics. 5.
https://doi.org/10.11591/eecsi.v5i5.1720
Yeh, Tom & Boris Katz. “Searching documentation using text, OCR, and image”. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '09). Association for Computing Machinery, New York, NY, USA, 776–777. 2009
https://doi.org/10.1145/1571941.1572123
Karami, E., Prasad, S., & Shehata, M.. “Image Matching Using SIFT, SURF, BRIEF, and ORB: Performance Comparison for Distorted Images”. ArXiv, 2017.
https://arxiv.org/abs/1710.02726
S. A. K. Tareen and Z. Saleem, "A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK," 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018, pp. 1-10
https://doi.org/10.1109/ICOMET.2018.8346440
A. Jakubović and J. Velagić, "Image Feature Matching and Object Detection Using Brute-Force Matchers," 2018 International Symposium ELMAR, Zadar, pp. 83-86. 2018
http://doi.org/10.23919/ELMAR.2018.8534641
Arlazarov, V.V., K. Bulatov, T. Chernov, and V.L. Arlazarov. “MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream”. ArXiv, 2019.
https://doi.org/10.18287/2412-6179-2019-43-5-818-824
Gu, Shanqing; Pednekar, Manisha; and Slater, Robert "Improve Image Classification Using Data Augmentation and Neural Networks”. SMU Data Science Review: Vol. 2: No. 2, Article 1. 2019
https://scholar.smu.edu/datasciencereview/vol2/iss2/1
Casado-García, Á., Domínguez, C., García-Domínguez, M. “CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks”. BMC Bioinformatics. 2019
https://doi.org/10.1186/s12859-019-2931-1
K. Wang, B. Fang, J. Qian, S. Yang, X. Zhou, and J. Zhou. “Perspective Transformation Data Augmentation for Object Detection”. IEEE Access, 2020, vol. 8, pp. 4935-4943. 2020
https://doi.org/10.1109/ACCESS.2019.2962572
Srivast, Shrey & Divekar, Amit & Anilkumar, Chandu & Naik, Ishika & Kulkarni, Ved & V., Pattabiraman. "Comparative Analysis of Deep Learning Image Detection Algorithms". 2020. J Big Data 8, pp: 66.
https://doi.org/10.1186/s40537-021-00434-w
Wu, Yuxin, Alexander K., Francisco M., Wan-Yen L. and Ross G. "Detectron2". 2019. Facebook. Available online: https://github.com/facebookresearch/detectron2 (accessed on March 18, 2020).
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," 2011 International Conference on Computer Vision, Barcelona, pp. 2564-2571. 2011
https://doi.org/10.1109/ICCV.2011.6126544
Luo, Chuan & Yang, Wei & Huang, Panling & Zhou, Jun. "Overview of Image Matching Based on ORB Algorithm". 2019. Journal of Physics: Conference Series
https://doi.org/10.1088/1742-6596/1237/3/032020.
Henderson, Paul & Ferrari, Vittorio. "End-to-End Training of Object Class Detectors for Mean Average Precision". 2017. ACCV 2016: Computer Vision – ACCV 2016 pp 198-213.
https://doi.org/10.1007/978-3-319-54193-8_13
Lowe, D.G. “Distinctive Image Features from Scale-Invariant Keypoints”. International Journal of Computer Vision 60, 91–110. 2004
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