Fingerprint Identification for Attendance Using Euclidean Distance and 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.