The Application of The Manhattan Method to Human Face Recognition

  • Sunardi Universitas Ahmad Dahlan
  • Abdul Fadlil Universitas Ahmad Dahlan
  • Novi Tristanti Universitas ahmad Dahlan
Keywords: face, detection manhattan, image

Abstract

In face recognition, the input image used will be converted into a simple image, which will then be analyzed. The analysis was carried out by calculating the distance of data similarity. In the process of measuring data similarity distances, they often experience problems implementing complex algorithm formulas. This research will solve this problem by implementing the Manhattan method as a method of measuring data similarity distances. In this study, it is hoped that the Manhattan method can be used properly in the process of matching test images and training images by calculating the proximity distance between the two variables. The distance sought is the shortest distance; the smaller the distance obtained, the higher the level of data compatibility. The image used in this study was converted into grayscale to facilitate the facial recognition process by thresholding, namely the process of converting a grayscale image into a binary image. The binary image of the test data is compared with the binary image of the training data. The image used in this study is in the Joint Photographic Experts Group (JPEG) format. Testing was carried out with 20 respondents, with each having two training images and two test images. The research was conducted by conducting experiments as many as 20 times. Facial recognition research using the Manhattan method obtains an accuracy of 70%. The image lighting used as the dataset influenced the accuracy results obtained in this study. Based on the results of this study, it can be concluded that the Manhattan method is not good for use in facial recognition research with poor lighting.

Downloads

Download data is not yet available.

References

A. Eleyan and M. S. Anwar, “Multiresolution Edge Detection Using Particle Swarm Optimization,” Int. J. Eng. Sci. Appl., vol. 1, no. 1, pp. 11–17, 2017.

M. A. Siddiq, I. Santoso, and A. A. Zahra, “Identifikasi Wajah Manusia Dengan Analisis Komponen Bebas,” J. Ilm. Tek. Elektro, vol. 6, no. 2, pp. 254–259, 2017.

Abdul Azis, Danar Putra Pamungkas, and Ahmad Bagus Setiawan, “Analisa Perbandingan Algoritma Euclidean Dan Manhattan Distance Dalam Identifikasi Wajah,” Semin. Nas. Inov. Teknol., pp. 219–224, 2021.

A. Azis, D. P. Pamungkas, and A. B. Setiawan, “Analisa Perbandingan Algoritma Euclidean Dan Manhattan Distance,” Semin. Nas. Inov. Teknol., pp. 219–224, 2021.

A. Saleh, A. F K Sibero, and I. H G Manurung, “Pengenalan Tanaman Herbal Menggunakan Algoritma Learning Vector Quantization Dan Manhattan Distance,” J. TEKESNOS, vol. 3, no. 2, pp. 271–276, 2021.

Y. Miftahuddin, S. Umaroh, and F. R. Karim, “Perbandingan Metode Perhitungan Jarak Euclidean , Haversine , ( Studi Kasus : Institut Teknologi Nasional Bandung ),” vol. 14, no. 2, pp. 69–77, 2020.

K. N. Using and P. Swarm, “Optimalisasi Pengenalan Wajah Berbasis Linear Discriminant Analysis Dan K-Nearest Neighbor Menggunakan Particle Swarm Optimization ( Optimization Of Face Recognition Based On Linear Discriminant Analysis And,” vol. 4, no. 1, pp. 40–51, 2022.

M. Faisal, E. M. Zamzami, and Sutarman, “Comparative Analysis of Inter-Centroid K-Means Performance using Euclidean Distance, Canberra Distance and Manhattan Distance,” J. Phys. Conf. Ser., vol. 1566, no. 1, 2020, doi: 10.1088/1742-6596/1566/1/012112.

F. Yue et al., “High-resolution grayscale image hidden in a laser beam,” no. August 2017, pp. 1–6, 2018, doi: 10.1038/lsa.2017.129.

I. Žeger, S. Grgic, and J. Vuković, “Grayscale Image Colorization Methods : Overview and Evaluation,” vol. 9, 2021, doi: 10.1109/ACCESS.2021.3104515.

M. Thresholding, D. Otsu, and S. Bhahri, “Transformasi Citra Biner Menggunakan,” vol. 7, no. 2, pp. 195–203, 2018.

M. Hendriani, Rais, and L. Handayani, “Penerapan Artificial Neural Network Terhadap Identifikasi Wajah Menggunakan Metode Backpropagation,” Nat. Sci. J. Sci. Technol., vol. 8, no. 3, pp. 203–208, 2019, doi: 10.22487/25411969.2019.v8.i3.14599.

D. I. S. Saputra, R. A. Pamungkas, K. A. N. Ramadhan, and W. S. Anjar, “Pelacakan Dan Deteksi Wajah Menggunakan Video Langsung Pada Webcam,” Telematika, vol. 10, no. 1, pp. 50–59, 2017.

N. W. Pratiwi, F. Fauziah, S. Andryana, and A. Gunaryati, “Deteksi Wajah Menggunakan Hidden Markov Model (HMM) Berbasis Matlab,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 3, no. 1, p. 44, 2018, doi: 10.30998/string.v3i1.2538.

A. D. M. Africa, A. J. A. Abello, Z. G. Gacuya, I. K. A. Naco, and V. A. R. Valdes, “Face recognition using MATLAB,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 8, no. 4, pp. 1110–1116, 2019, doi: 10.30534/ijatcse/2019/17842019.

A. A. Abdulrahman and F. S. Tahir, “Face recognition using enhancement discrete wavelet transform based on MATLAB,” Indones. J. Electr. Eng. Comput. Sci., vol. 23, no. 2, pp. 1128–1136, 2021, doi: 10.11591/ijeecs.v23.i2.pp1128-1136.

Published
2022-12-28
How to Cite
Sunardi, Abdul Fadlil, & Novi Tristanti. (2022). The Application of The Manhattan Method to Human Face Recognition. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(6), 939 - 944. https://doi.org/10.29207/resti.v6i6.4265
Section
Artikel Teknologi Informasi

Most read articles by the same author(s)