Mesin Absensi Face Recognition Berbasis Raspberry Pi

R. Faulianur, Inzar Salfikar, Ryan Mulyawan
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引用次数: 1

Abstract

The fingerprint attendance machine failed to record attendance with injured, scratched, peeled finger skin and others so that attendance was not recorded. This research is a development of previous research with Radio Frequency Identification (RFID) attendance system because attendance system with RFID can be rigged. The incorporation of a facial recognition system and RFID on an attendance machine with a raspberry pi is expected to minimize failures during attendance. Because if there is a facial recognition failure, the user can make attendance with RFID. Attendance with RFID in this system can only be done when there is a face detection failure. To find out the percentage of success and accuracy of the machine, each user performs several trials. Furthermore, the number of facial recognition successes was recorded and the accuracy value was calculated using the accuracy calculation method. The results of the face identification experiment showed that the accuracy of the first user was 53%, the second user was 48%, the third user was 45% and the fourth user was 52%. The machine is able to predict 4 user images with 4 different face positions with an average identification process time of 7 seconds.
基于树莓派的缺席人脸识别
指纹考勤机因手指受伤、划伤、手指皮肤剥落等原因无法记录考勤,导致考勤不记录。本研究是在以往射频识别考勤系统研究的基础上发展起来的,因为射频识别考勤系统具有可操控性。将面部识别系统和射频识别技术集成到一个带有树莓派的考勤机上,有望将考勤故障降至最低。因为如果面部识别失败,用户可以使用RFID来考勤。在这个系统中,只有当人脸检测失败时,才能使用RFID考勤。为了找出机器的成功率和准确性,每个用户都要进行几次试验。在此基础上,记录人脸识别成功次数,并采用准确率计算方法计算准确率值。人脸识别实验结果表明,第一个用户的准确率为53%,第二个用户为48%,第三个用户为45%,第四个用户为52%。该机器能够预测4张不同面部位置的用户图像,平均识别时间为7秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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