Detección de Personal no Autorizado en el Departamento de ti Utilizando Redes Neuronales Convolucionales en Tiempo Real con Raspberry Pi 3 B+

Translated title of the contribution: Detection of Unauthorized Personnel in the IT Department Using Convolutional Neural Networks in Real Time with Raspberry Pi 3 B+

Miguel Angel Quiroz Martinez, Jonathan Benjamin Prieto Villamar, Galo Enrique Valverde Landivar, Rosario Luis Apupalo Del

Research output: Contribution to journalArticle

Abstract

This project details the development of a facial recognition system and the sending of alerts via email. To carry it out, the TensorFlow, Numpy and OpenCV libraries were used. In addition, a Pi camera V2 and a Raspberry Pi 3 B+. The libraries in charge of aligning the faces of the people to be detected and then using these "aligned" faces to train the neural network used in the application are TensorFlow and Numpy. OpenCV to initiate video capture and object detection in real time on a Raspberry Pi 3 environment, in charge of processing and storing the database with the photos of the faces. The alerts are sent via email for each undetected face and lighting a red LED. This facial recognition system was implemented with the objective of detecting faces that are not in the database. The authentication system was performed on the Raspberry Pi 3, with the help of TensorFlow, Numpy and OpenCV libraries, the results obtained in this study were favorable after an evaluation on the database of 10 people. This system works perfectly with the database, it studies, learns the captured faces and compares them with those stored in the database; and will alert when it detects a face not found.
Translated title of the contributionDetection of Unauthorized Personnel in the IT Department Using Convolutional Neural Networks in Real Time with Raspberry Pi 3 B+
Original languageSpanish (Ecuador)
Pages (from-to)49-60
JournalJournal of Science and Research
Volume5
Issue number5
DOIs
StatePublished - 3 Jul 2020

Keywords

  • Algorithms
  • Deep learning
  • Mml

CACES Knowledge Areas

  • 116A Computer Science

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