Zulkarnain Sudirman

Work place: STMIK Widya Cipta Dharma, 75123, Indonesia

E-mail: zlkrnsudirman@wicida.ac.id

Website:

Research Interests: Computational Science and Engineering, Computational Engineering, Computer systems and computational processes, Computer Architecture and Organization, Engineering

Biography

Zulkarnain Sudirman, son of Sudirman, S.Pdi and Darmawati,. He is an undergraduate student from Computer Engineering Department, STMIK Widya Cipta Dharma. He obtained Bachelor's Degree from STMIK Widya Cipta Dharma in 2020. He focuses on research and development tools with TensorFlow. His Hobby is playing game.

Author Articles
Face Recognition Using Histogram of Oriented Gradients with TensorFlow in Surveillance Camera on Raspberry Pi

By Reza Andrea Nurul Ikhsan Zulkarnain Sudirman

DOI: https://doi.org/10.5815/ijieeb.2022.01.05, Pub. Date: 8 Feb. 2022

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.

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