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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Hernanda Agung Saputra
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Deteksi dan Pengenalan Wajah sebagai Pendukung Keamanan Menggunakan Algoritme Haar-Classifier dan Eigenface Berbasis Raspberry Pi Hernanda Agung Saputra; Fitri Utaminingrum; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the things that is inseparable from the progress of technology is security. If we only rely on a security system using human power, it is also not so effective because people also have a sense of tired. Therefore a security support system was created such as barcode, rfid card, PIN, password and etc. However, the use of media that has some security flaws namely can be lost, stolen or damaged, and abused by people who are not responsible. One of the alternatives that can be performed i.e. utilize face as data security. On the research of this system are made using Raspberries Pi 3 were integrated with the Logitech webcam C525 as input, as well as the mikrokontroller Arduino Uno as ultrasonic and light sensor processing. For LCD, buzzer, and module SIM800L is used as the output of the system to provide notification in the form of a alarm,visual text, and SMS. This system uses Haar-Classifier to detect face objects in the image captured by the webcam. Next, Eigenface method is used to get weight of face image. After weight of face image obtained, search the smallest difference in weight of face image of new faces with the image of the face on the database where the results determine how the output from the system. From the results of testing the accuracy of face detection, best accuracy is obtained at a distance of 40 cm with 100% accuracy. Overall accuracy of testing the accuracy of face recognition at a distance of 40 cm is 75%. From system integration testing software with hardware obtained percentage error of 0%. The average time of computation in recognizing a face is 0.11536 seconds.
Co-Authors Utaminingrum, Fitri Wijaya Kurniawan