Automatic and Secure Electronic Gate System Using Fusion of License Plate, Car Make Recognition and Face Detection
Author | Saadouli G. |
Author | Elburdani M.I. |
Author | Al-Qatouni R.M. |
Author | Kunhoth S. |
Author | Al-Maadeed, Somaya |
Available date | 2022-05-19T10:23:10Z |
Publication Date | 2020 |
Publication Name | 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/ICIoT48696.2020.9089615 |
Abstract | In this paper, we propose an automatic electronic gate system with authenticity confirmation from 3 individual modules, viz., car make and model, license plate and face detection. The ultrasonic sensor detects the stopped cars and initiate the camera to capture the car image. Connected Components identification and Optical Character Recognition (OCR) algorithms are performed to recognize the characters and numbers in the car plate. The car make and model detection algorithm uses feature extraction algorithms based on Difference of Gaussians (DoG) detector and Scale Invariant Feature Transform (SIFT) descriptor. The Euclidian distance measure identifies the suitable match for a query image with the one in database to determine the car make and model. The face detection of driver is carried out to ensure the security of intended premise of the system deployment. It is based on the Viola Jones algorithm. In the final stage, matching algorithms are applied to decide whether the image of the car plate, make and model and the face does not conflict with the details stored in the database. The smart electronic gate will open and let the car enter when the authenticity is confirmed. The system is evaluated by building a prototype electronic smart gate using Logitech C920 HD pro webcam, Toy car and LEGO NXT Motor Controlled Gate. Moreover we built a preliminary database from the images captured by surveillance cameras for make and model recognition of Qatar cars. Experimental evaluation on this dataset delivered an accuracy of 75%. |
Sponsor | ACKNOWLEDGMENT This publication was supported by Qatar University Collaborative High Impact Grant QUHI-CENG-18/19-1. The findings achieved herein are solely the responsibility of the authors. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar University. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Authentication Cameras Database systems Feature extraction Internet of things License plates (automobile) Optical character recognition Query processing Security systems Ultrasonic applications Building a prototypes Difference of Gaussians Experimental evaluation Feature extraction algorithms Optical character recognition (OCR) Scale invariant feature transforms Surveillance cameras Viola - Jones algorithms Face recognition |
Type | Conference |
Pagination | 79-84 |
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Computer Science & Engineering [2426 items ]