International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 14, No. 1, 8 Feb. 2022
Cover page and Table of Contents: PDF (size: 649KB)
Face Recognition Using Histogram of Oriented Gradients with TensorFlow in Surveillance Camera on Raspberry Pi
Full Text (PDF, 649KB), PP.46-52
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Author(s)
Index Terms
Face Recognition, Deep Learning, TensorFlow, Surveillance Camera, Raspberry Pi
Abstract
The implementation of face recognition with TensorFlow deep learning uses the webcam as a surveillance camera on the Raspberry Pi, aiming to provide a sense of security to the requiring party. A frequent surveillance camera problem is that crimes are performed at certain hours, the absence of early warning features, and there is no application of facial recognition on surveillance cameras. The function of this system is to perform facial recognition on every face captured by the webcam. Use the Histogram of the Oriented Gradient (HOG) method for the extraction process of deep learning. The image that is input from the camera will undergo a gray scaling process, then it will be taken the extraction value and classified by deep learning framework with TensorFlow. The system will send notifications when faces are not recognized. Based on the analysis of the data is done, the conclusion that the implementation of face recognition is built on the Raspberry Pi using a Python programming language with the help of TensorFlow so that the training process of the sample is much faster and more accurate. It uses a Graphical User Interface (GUI) as the main display and is built using Python designer, using email as an initial warning delivery medium to the user as well as using the webcam as the main camera to capture image.
Cite This Paper
Reza Andrea, Nurul Ikhsan, Zulkarnain Sudirman, "Face Recognition Using Histogram of Oriented Gradients with TensorFlow in Surveillance Camera on Raspberry Pi", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.14, No.1, pp. 46-52, 2022. DOI:10.5815/ijieeb.2022.01.05
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