Open AccessSmart Home Security Menggunakan Face Recognition Dengan Metode Eigenface Berbasis Raspberry Pi
Author(s) -
Rudi Kurniawan,
Antoni Zulius
Publication year - 2019
Publication title -
jurnal sustainable
Language(s) - English
Resource type - Journals
eISSN - 2615-6334
pISSN - 2087-5347
DOI - 10.31629/sustainable.v8i2.1484
Subject(s) - eigenface , facial recognition system , computer science , artificial intelligence , rgb color model , biometrics , pattern recognition (psychology) , three dimensional face recognition , artificial neural network , computer vision , grayscale , feature (linguistics) , face detection , image (mathematics) , linguistics , philosophy
One of the biometric technologies that have been implemented in many security systems besides retinal recognition, fingerprint recognition and iris is facial recognition. On the hardware side itself, face recognition (Face Recognition) uses a camera to capture a person's face then compared to the previous face that has been stored in a particular database. There are several methods of facial recognition, namely neural networks, artificial neural networks, adaptive neuro fuzzy, and eigenface. Specifically in this study the method to be explained is the eigenface method. Specifically in this study the method that will be explained is the eigenface method, and uses a web cam to capture images in real time. The advantage of this method is that the computation is very fast and simple compared to the use of methods that require a lot of learning, such as artificial network requirements. Broadly speaking, the process of this application is the camera to capture faces, then an RGB value is obtained. Using the initial processing, resize, RGB to Grayscale, and histogram equalization for light alignment. The eigenface method functions to calculate the eigenvalue, and the eigenvector that will be used as a feature in making recognition. From the experiments and tests carried out, the tool can recognize facial images with a success rate of up to 90% at a distance of 25 cm with an average success of 72.5%. This proves this tool is quite good in face recognition.