Counting Students using OpenCV and Integration with Firebase for Classroom Allocation

Mehariya, Jahanvi; Gupta, Chaitra; Pai, Niranjan; Koul, Sagar; Gadakh, Prashant

doi:10.1109/icesc48915.2020.9155825

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…On the other hand, it is observed that the haar cascade algorithm from the OpenCV library has outperform other techniques that have been used in face recognition [49,52,53]. The haar cascade algorithm has constantly achieved accuracy that ranges from 90% to 100% [49,53].…”

Section: Discussionmentioning

confidence: 99%

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Access Control System for Covid19 Using Computer Vision and Deep Learning Techniques: A Aystematic Review

Abiodun

Ogbuju

Oladipo

2022

J. Appl. Artif. Intell.

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After the emergence of the Coronavirus disease (Covid19) in December, 2019 the virus was confirmed by the World Health Organization (WHO) to be a dangerous virus that spread through airborne and droplets. One of the most effective way of preventing the spread of this virus is the use of facemask in public places such as banks, shopping mall, schools, offices and the likes. For this reasons people are advised to use facemask whenever they go out for their daily activities. However, some people have refused to wear the facemask in public places, thereby increasing the rate at which the virus spreads. Hence, there is a need to design an access control system for use in public places to grant access to people complying with the facemask regulations or deny access to those faulting them. Literature has shown that the use of computer vision and deep learning technology can play important role in the deployment of such access control system for face detection and facemask detection. The computer vision and deep learning techniques used in face recognition and facemask detection are describes in this systematic literature review.

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Section: Discussionmentioning

confidence: 99%

“…An attendance system that can take attendance of student in a classroom, or workers by detecting their faces was developed by [53]. Images of students were taken and used for the training of the model.…”

mentioning

confidence: 99%

Access Control System for Covid19 Using Computer Vision and Deep Learning Techniques: A Aystematic Review

Abiodun

Ogbuju

Oladipo

2022

J. Appl. Artif. Intell.

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“…We have used PCA with advanced features that can outperform many other methods when the database is a little smaller. For the comparative analysis, we selected three OpenCV-based FR methods from the literature that are used to verify the authenticity of the user [10], [18] and [19]. The results of the analysis are shown graphically in Fig.…”

Section: Comparison With Existing Approaches In Literaturementioning

confidence: 99%

Cost Efficient Automobile Security Using Face Recognition and GSM Module

Gulzar

Qadeer

Aamir

et al. 2022

JCBI

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Face detection is one of the most important modern computer vision topics. The importance of face recognition (FR) is increasing in our society for identification purposes. In this paper, we practically demonstrated the authenticity of an automatic security system for automobiles using face recognition technology. Our proposed system is not only a facial recognition system, but also supports authentication via a GSM module for remote monitoring and permission/control by a specific remote security code. The system was implemented and tested using a Raspberry Pi 3 board, Python and OpenCV to program the different modules for face recognition. The implementation results confirm the applicability of the proposed system as it offers the advantages of robustness, high accuracy, reliability and security at the lowest cost. This paper defines a combination of fail-safe algorithms for the GPS/GSM module in addition to face recognition algorithms, which are more suitable in dynamic environments where high uncertainties are expected and require high accuracy and reasonable speed. In future, we aim to develop an intelligent smart home application to meet the upcoming needs.

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“…The SSD method is focused on the feed-forward convolutional network that generates a permanent border-box array and results in the existence of class-based entity instances in these boxes and a non-maximum deletion stage to generate final detection. The early network layers are built on a common image classification design [16], [20]. Fig.…”

Section: Single Shot Detectors (Ssds)mentioning

confidence: 99%

“…Mehariya et al [20] designed a system to overcome the problem of students not attending lectures in classrooms in universities or any place requiring workers' attendance. As non-attendance is a waste of the infrastructure, manual absence management is a tedious task and a waste of time, as the traditional absence counting system may be cheated.…”

Section: Face Detectionmentioning

confidence: 99%

Face Detection and Recognition Using Opencv

Suganthi¹,

Poongothai²,

Prashanthini³

et al. 2020

GOR

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Computer Vision is one of the most fascinating and challenging tasks in the field of Artificial Intelligence. Computer Vision serves as a link between computer software and the visuals we see around us. It enables computer software to comprehend and learn about the visuals in its environment. As an example: The fruit is determined by its color, shape, and size. This job may seem simple for the human brain, but in the Computer Vision pipeline, we first collect data, then conduct data processing operations, and then train and educate the model to learn how to differentiate between fruits based on size, shape, and color. The main goal is to identify and comprehend the images and offer new images that are more useful for us in different life fieldsThe term "OpenCV" is an abbreviation for "open source computer vision." The architecture is made up of software, databases, and plugins that are pre-programmed with support for integrating computer vision applications [3]. It is one of the most used toolkits with a large developer group. It is well-known for the size at which it builds realworld usage cases for industrial use. OpenCV follows C/C++, Python, Java programming languages and can be used to build computer vision software for desktop and smartphone platforms such as Windows, Linux, macOS, Android, and iOS. The most recent releases are OpenCV-4.5.2 and OpenCV-3.4.14. It is free and open-source, as well as simple to use and install. It is intended for numerical productivity with a heavy emphasis on real-time applications. The first version was in the C programming language; however, its success increased with the release of Version 2.0, which had a C++ implementation [2]. C++ is used to create new features. OpenCV can be downloaded for free from http://opencv.org. This platform includes the most recent distribution update (version: 4.5.2) as well as older iterations. Photos must be in BGR or Grayscale format in order to be displayed or saved via OpenCV. Otherwise, unfavorable outcomes could occur [1].Face detection is a form of computer vision that aids in detecting and visualizing facial features in captured pictures or real-time videos. This type of object detection technique detects instances of semantic artifacts of a given class (such as people, cars, and houses) in digital pictures and videos. Face recognition has become increasingly important as technology has advanced, especially in fields such as photography, defense, and marketing [4], [5].

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Counting Students using OpenCV and Integration with Firebase for Classroom Allocation

Cited by 8 publications

References 3 publications

Access Control System for Covid19 Using Computer Vision and Deep Learning Techniques: A Aystematic Review

Access Control System for Covid19 Using Computer Vision and Deep Learning Techniques: A Aystematic Review

Cost Efficient Automobile Security Using Face Recognition and GSM Module

Face Detection and Recognition Using Opencv

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