A LabVIEW Design for Frontal and Non-Frontal Human Face Detection System in Complex Background

Mariappan, Muralindran; Nadarajan, Manimehala; Porle, Rosalyn R.; Ramu, Vigneswaran; Thiam, Brendan Khoo Teng

doi:10.4028/www.scientific.net/amm.490-491.1259

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…We are trying to identify a particular object in the facial region of the real-time image. The methods mentioned in [6] are useful for colour plane extraction and methods in [7] to point of the face region from the complex backgrounds of the image fed into system but this is not useful in the present case as most of the facial features are masked/covered due to the mask which results in abnormalities for further processing of the real-time image [8][9][10].…”

Section: Literature Surveymentioning

confidence: 99%

Face Mask Detection Using LabView

et al. 2021

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<p>Rapid worldwide spread of Corona virus Disease 2019 (COVID 19) has resulted in a global pandemic. In present scenario due to covid-19, the mask has been an important part of our live for our safety as well as for the others safety so there is a need for efficient face mask detection applications in crowded areas like shopping malls, Public transportation etc. To ensure safety of the people in the surroundings. Face Mask Detection using NI LabVIEW. In this project a real-time system is developed to detect whether the person is wearing a mask or not by acquiring a real-time image of him through a Camera. The main challenges in detecting the mask are there are masks with various colors and patterns and secondly the background, light intensity are also the factors that affect the result. So, all these factors should be taken into consideration while developing the system in real-time. This system used for this application consists of vision development module. Vision development module helps to develop applications for machine vision and image processing applications we can use it with LabVIEW for real- time systems. A camera with good pixel quality is used for image acquisition. The captured image is of RGB format, it is difficult to analyze the image in this format, so it undergoes color plane extraction in this only a single plane of the image is considered which separates the mask from surroundings and results in a grey scale image for further processing. The image later is compared to a custom-made template dataset using pattern matching algorithm from vision assistant which helps to detect the mask region. overlaying techniques are used to highlight the mask region which shows that the person is wearing the mask.<strong></strong></p>

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Section: Literature Surveymentioning

confidence: 99%

Face Mask Detection Using LabView

et al. 2021

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“…system There are many methods implemented for face detection, one of the method is by using skin segmentation process.In this method, detecting face in a complex background is possible, when the image is given it will detect the skin cells by using threshold and image processing techniques [7], the time complexities will increase with the increase in size as it has to detect every pixel in the image. And face recognition can be done by using histogram matching technique under certain limitations, as the intensity of the natural light will have a huge impact on this technique and as the light intensity changes, the pixel values also changes, which results in error in the system [8].…”

Section: Figure1: Block Diagram For Attendance Managementmentioning

confidence: 99%

Attendance Management System using Face Recognition

Sujatha¹,

Bharathi²,

Shanthi³

2019

IJITEE

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Attendance management system is one of the tremendous challenges in any organization to reduce the malpractices by the individual. This paper aims to design automatic system for attendance using the face detection using LabVIEW (Vision Assistance Module) in order to replace the manual system and makes easier for the user to calculate the number of individuals and reduces the burden in taking attendance. Face recognition is one of the biometrics used in security systems, human machine interaction and image processing techniques [5]. This system is mostly helpful in security purpose and in commercial applications. It can be done by taking the image of the individual which is been captured by camera so that this image is converted into digital form. From this digital data the pattern of individuals is extracted so that everyone will have unique patterns. These patterns are stored in the database in excel format. The identification of the person is done by comparing the image captured through camera with the database images. Initially before starting the Attendance every individual is marked absent. Once the individual comes across the camera, the Automated system will extract the information by pre-processing the image with the help of Vision Assistance and extracts the patterns from the digital data. This data is compared with the stored patterns in database if the pattern matches with the database it automatically marks present

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“…In [112,135,136,137], boundary models based on skin locus in other perceptual color spaces are proposed.…”

Section: A Explicitly Defined Boundary Modelsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

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Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

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A LabVIEW Design for Frontal and Non-Frontal Human Face Detection System in Complex Background

Cited by 5 publications

References 9 publications

Face Mask Detection Using LabView

Face Mask Detection Using LabView

Attendance Management System using Face Recognition

A Comprehensive Survey on Human Skin Detection

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