The Application of The Manhattan Method to Human Face Recognition
Dublin Core
Title
The Application of The Manhattan Method to Human Face Recognition
Subject
face, detection, manhattan, image
Description
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
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
Creator
Sunardi1
1
, Abdul Fadlil2
, Novi Tristanti3
1
, Abdul Fadlil2
, Novi Tristanti3
Publisher
Ahmad Dahlan University, Yogyakarta, Indonesia
Date
28-12-2022
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Sunardi1
1
, Abdul Fadlil2
, Novi Tristanti3, “The Application of The Manhattan Method to Human Face Recognition,” Repository Horizon University Indonesia, accessed June 28, 2025, https://repository.horizon.ac.id/items/show/9282.