Komparasi Algoritma Fitur Matching SIFT Dan AKAZE Untuk Pencocokan Fitur Wajah Berbasis Citra

Galih Putra Pratama; Husin Fadhil Azizi; Tira Karel Agata; Muhammad Naufal

doi:10.62866/jurikti.v4i1.264

Galih Putra Pratama Universitas Dian Nuswantoro, Kota Semarang, Indonesia
Husin Fadhil Azizi Universitas Dian Nuswantoro, Kota Semarang, Indonesia
Tira Karel Agata Universitas Dian Nuswantoro, Kota Semarang, Indonesia
Muhammad Naufal * Universitas Dian Nuswantoro, Kota Semarang, Indonesia https://orcid.org/0009-0002-2893-7826

(*) Corresponding Author

DOI: https://doi.org/10.62866/jurikti.v4i1.264

Keywords: SIFT; AKAZE; Face Recognition; Keypoint; NIST Dataset

Abstract

The problem of matching facial features is an important challenge in biometric systems, especially due to variations in lighting, texture and facial details that affect the stability of keypoint detection. This research aims to compare the performance of the Scale-Invariant Feature Transform (SIFT) and Accelerated-KAZE (AKAZE) algorithms in the facial feature extraction and matching process to determine the trade-off between accuracy and computational efficiency. The dataset used comes from NIST with 393 training images and 341 validation images. Evaluation is carried out using the number of detected keypoints, number of matching keypoints, number of inliers and outliers, feature extraction time, as well as error metrics such as MSE, MAE, RMSE, and R². Experimental results show that SIFT produces better matching performance with a total of 934,763 keypoints detected, an average matching keypoint of 121.14, and the number of inliers of 116.95. In addition, SIFT produces lower MSE, MAE, and RMSE values than AKAZE, indicating better feature matching consistency in facial images. However, AKAZE has higher computational efficiency with an average feature extraction time of 0.1699 seconds, faster than SIFT of 0.2928 seconds. The contribution of this research lies in the comparative analysis of the performance of SIFT and AKAZE in keypoint-based facial feature matching, so that it can be a reference in selecting algorithms according to application needs, both oriented towards accuracy and computational efficiency.

References

D. R. I. M. Setiadi, S. Rustad, P. N. Andono, and G. F. Shidik, “Digital image steganography survey and investigation (goal, assessment, method, development, and dataset),” Signal Processing, vol. 206, p. 108908, May 2023, doi: 10.1016/j.sigpro.2022.108908.

J. Guo, Y. Chen, J. Li, and J. Xie, “Face feature matching based on semantic Information,” J. Phys.: Conf. Ser., vol. 2024, no. 1, p. 012015, Sep. 2021, doi: 10.1088/1742-6596/2024/1/012015.

S. Dalal, V. P. Vishwakarma, and S. Kumar, “Feature-based Sketch-Photo Matching for Face Recognition,” Procedia Computer Science, vol. 167, pp. 562–570, 2020, doi: 10.1016/j.procs.2020.03.318.

S. Jha, “Computer Vision for Surveillance and Monitoring,” in 2025 5th International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), MANDYA, India: IEEE, Sep. 2025, pp. 1–5. doi: 10.1109/ICERECT65215.2025.11376098.

P. Singh, M. Rattan, N. Singh Grewal, and G. Aggarwal, “Distributed Feature Matching for Robust Object Localization in Robotic Manipulation,” IEEE Access, vol. 12, pp. 161679–161687, 2024, doi: 10.1109/ACCESS.2024.3482428.

A. Widya Agata, W. S J Saputra, and C. Aji Putra, “Pengenalan Bahasa Isyarat Indonesia (BISINDO) Menggunakan Algoritma Scale Invariant Feature Transform (SIFT) Dan Convolutional Neural Network (CNN),” jati, vol. 8, no. 1, pp. 1054–1061, Mar. 2024, doi: 10.36040/jati.v8i1.8917.

M. Adhitya Syahputra and M. Abdi Prawira Tanjung, “Perbandingan Metode Analisis Tekstur Dan Structural Matching Pada Citra Digital Untuk Identifikasi Produk Papan Arduino,” jati, vol. 8, no. 6, pp. 11868–11879, Nov. 2024, doi: 10.36040/jati.v8i6.11709.

P. R. Chintia Wardhani and M. Y. Florestiyanto, “Recognition Of Hiragana Japanese Handwriting Characters Using Support Vector Machine And Scale Invariant Feature Transform,” Telematika, vol. 21, no. 2, p. 135, Jun. 2024, doi: 10.31315/telematika.v21i2.12042.

F. D. Adhinata, R. Adhitama, and A. J. T. Segara, “Real-time currency recognition on video using AKAZE algorithm,” J. Teknol. dan Sist. Komput, vol. 9, no. 4, pp. 191–198, Oct. 2021, doi: 10.14710/jtsiskom.2021.13970.

S. K. Sharma and K. Jain, “Image Stitching using AKAZE Features,” J Indian Soc Remote Sens, vol. 48, no. 10, pp. 1389–1401, Oct. 2020, doi: 10.1007/s12524-020-01163-y.

S. Castillo-Carrión and J.-E. Guerrero-Ginel, “SIFT optimization and automation for matching images from multiple temporal sources,” International Journal of Applied Earth Observation and Geoinformation, vol. 57, pp. 113–122, May 2017, doi: 10.1016/j.jag.2016.12.017.

E. R. Runtuwene, S. Kumajas, and Q. Kainde, “Face Identification Using Image Procesing With The National Institute Of Standards And Technology (NIST) Method,” J. Tek. Inform. (JUTIF), vol. 5, no. 4, pp. 375–384, Aug. 2024, doi: 10.52436/1.jutif.2024.5.4.2350.

A. J. Amron, H. F. Azizi, M. L. Z. Arofi, C. Paramita, and M. Naufal, “Perbandingan Performa SIFT dan ORB dalam Pengolahan Dataset Wajah nist_2,” Jutisi J. Tek. Sis. Info, vol. 14, no. 2, p. 821, Aug. 2025, doi: 10.35889/jutisi.v14i2.2738.

S. N. Khonina, N. L. Kazanskiy, and M. A. Butt, “Grayscale Lithography and a Brief Introduction to Other Widely Used Lithographic Methods: A State-of-the-Art Review,” Micromachines, vol. 15, no. 11, p. 1321, Oct. 2024, doi: 10.3390/mi15111321.

S. M. Robeson and C. J. Willmott, “Decomposition of the mean absolute error (MAE) into systematic and unsystematic components,” PLoS ONE, vol. 18, no. 2, p. e0279774, Feb. 2023, doi: 10.1371/journal.pone.0279774.

M. J. Mayer and D. Yang, “Potential root mean square error skill score,” Journal of Renewable and Sustainable Energy, vol. 16, no. 1, p. 016501, Jan. 2024, doi: 10.1063/5.0187044.

K. Alim and D. Murni, “Prediksi Hasil Pertandingan Sepak Bola Liga Premier Inggris Dengan Artificial Neural Network Backpropagation,” Sci Tech Ed Math, vol. 4, no. 3, pp. 1523–1531, Dec. 2023, doi: 10.46306/lb.v4i3.425.

M. Naufal, H. Al Azies, F. Al Zami, and R. M. Brilianto, “Optimizing Driver Drowsiness Detection: Evaluating CLAHE and AHE Enhancement Techniques,” SISTEMASI, vol. 15, no. 2, p. 407, Feb. 2026, doi: 10.32520/stmsi.v15i2.5206.

F. K. T. Putri and A. D. Wowor, “Implementasi Algoritma Long Short-Term Memory dalam Prediksi Konsentrasi Gas Metana (CH4) di Kota Salatiga,” jtik, vol. 8, no. 2, pp. 418–426, Apr. 2024, doi: 10.35870/jtik.v8i2.1917.

Moch Farryz Rizkilloh and Sri Widiyanesti, “Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM),” J. RESTI (Rekayasa Sist. Teknol. Inf.), vol. 6, no. 1, pp. 25–31, Feb. 2022, doi: 10.29207/resti.v6i1.3630.

M. Naufal, H. Al Azies, G. A. Firmansyah, and N. M. K. Kharisma, “Penerapan Teknik Adaptive Dan Histogram Equalization Dalam Pengolahan Citra,” IlmuKomputer, vol. 5, no. 1, pp. 9–18, Mar. 2024, doi: 10.24127/ilmukomputer.v5i1.5345.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Komparasi Algoritma Fitur Matching SIFT Dan AKAZE Untuk Pencocokan Fitur Wajah Berbasis Citra