Face Detection With Face Mask for COVID-19 Pandemic Using Neural Networks

2022 ◽  
pp. 210-223
Author(s):  
Nitish Devendra Warbhe ◽  
Rutuja Rajendra Patil ◽  
Tarun Rajesh Shrivastava ◽  
Nutan V. Bansode
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Face Detection ◽  
Face Mask ◽  
Detection Technique ◽  
Facial Features ◽  
Feature Points ◽  
The People ◽  
Biometric Systems ◽  
The Face

The COVID-19 virus can be spread through contact and contaminated surfaces; therefore, typical biometric systems like password and fingerprint are unsafe. Face recognition solutions are safer without any need of touching any device. During the COVID-19 situation as all of the people are advised to wear masks on their faces, the existing face detection technique is not able to identify the person with face occlusion. The fraudsters and thieves take advantage of this scenario and misuse the face mask, favoring them to be able to steal and commit various crimes without being identified. Face recognition methods fail to detect or recognize the face as half of the face is masked and the features are suppressed. Face recognition requires the visibility of major facial features for face normalization, orientation correction, and recognition. Thus, the chapter focuses on the facial recognition based on the feature points surrounding the eye region rather than taking the whole face as a parameter.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

scholarly journals Mask Detection and Tracing System

2021 ◽  
pp. 406-412
Author(s):  
Sanket Shete ◽  
Kiran Tingre ◽  
Ajay Panchal ◽  
Vaibhav Tapse ◽  
Prof. Bhagyashri Vyas
Keyword(s):  
Neural Networks ◽  
Face Detection ◽  
Large Scale ◽  
Face Mask ◽  
Detection Algorithm ◽  
Detection Task ◽  
Loss Of Face ◽  
Satisfactory Performance ◽  
Training Samples ◽  
The Face

Covid19 has given a new identity for wearing a mask. It is meaningful when these masked faces are detected accurately and efficiently. As a unique face detection task, face mask detection is much more difficult because of extreme occlusions which leads to the loss of face details. Besides, there is almost no existing large-scale accurately labelled masked face dataset, which increase the difficulty of face mask detection. The system encourages to use CNN-based deep learning algorithms which has done vast progress towards researches in face detection In this paper, we propose novel CNN-based method which is formed of three convolutional neural networks to detect face mask. Besides, because of the shortage of face masked training samples, we propose a new dataset called” face mask dataset” to finetune our CNN models. We evaluate our proposed face mask detection algorithm on the face mask testing set, and it achieves satisfactory performance

scholarly journals Face Detection and Gender Classification using Facial Features

2019 ◽  
Vol 8 (4) ◽  
pp. 6670-6674
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Facial Features ◽  
Gender Classification ◽  
Biometric System ◽  
Detection Techniques ◽  
The Face ◽  
And Gender ◽  
Two Stages

Face Recognition is the most important part to identifying people in biometric system. It is the most usable biometric system. This paper focuses on human face recognition by calculating the facial features present in the image and recognizing the person using features. In every face recognition system follows the preprocessing, face detection techniques. In this paper mainly focused on Face detection and gender classification. They are performed in two stages, the first stage is face detection using an enhanced viola jones algorithm and the next stage is gender classification. Input to the video or surveillance that video converted into frames. Select few best frames from the video for detecting the face, before the particular image preprocessed using PSNR. After preprocessing face detection performed, and gender classification comparative analysis done by using a neural network classifier and LBP based classifier

scholarly journals Face with Mask Detection and Recognition for Smart Attendance System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1586-1591
Author(s):  
Shweta Panjabrao Dhawale
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Face Mask ◽  
Learning Framework ◽  
The Face ◽  
Face Detection And Recognition ◽  
Python Programming ◽  
Detection And Recognition

In this paper we will see the face mask detection and recognition for smart attendance system. In current pandemic situation our proposed system is very useful. We have used here face algorithm technique, python programming and to capture the images open cv is used., open cv2 now comes with a very new face recognizer class for the face recognition and popular computer vision liberaay started by intel in 1999. Open cv released under BSD licence that’s why used in the academic projects. We have used the concept of deep learning framework for the detection of faces. our aim is to present the study of previous attempts at face detection and recognition for smart attendance system by using deep learning .these is rapidly growing technology with its application in various aspects.

scholarly journals Implementasi Algoritma Hard Melokalisasi Fitur Wajah Pada Citra Warna

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Ujang Juhardi
Keyword(s):  
Face Recognition ◽  
Data Collection ◽  
Face Detection ◽  
Detection System ◽  
Early Stage ◽  
Recognition System ◽  
Image Resolution ◽  
Level Of Detail ◽  
Facial Features ◽  
The Face

AbstrakPendeteksian wajah (face detection) adalah salah satu tahap awal yang sangat penting dalam sistem pengenalan wajah (face recognition) yang digunakan dalam identifikasi biometrik. Sejauh ini, kendala utama yang dihadapi dalam sistem pendeteksian wajah berkisar pada masalah ukuran (resolusi citra), Resolusi citra merupakan tingkat detailnya suatu citra. Semakin tinggi resolusinya semakin tinggi pula tingkat detail dari citra tersebut. Haar like Feature merupakan metode yang lazim digunakan dalam pendeteksian obyek khususnya pendeteksian wajah dan fitur-fiturnya. Prinsip Haar-like features adalah mengenali obyek berdasarkan nilai sederhana dari fitur tetapi bukan merupakan nilai piksel dari image obyek tersebut. Metode ini memiliki kelebihan yaitu komputasinya sangat cepat, karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah image. Untuk mengimplementasikan dan menganalisis kecepatan hasil algoritma haar dalam melokalisasikan fitur wajah penelitian ini menggunakan software MATLAB R2012b agar dapat mengetahui bagaimana cara menganalis dan mengimplementasikan serta mendapatkan hasil menganalisis pengaruh resolusi citra dari algoritma haar dalam melokalisasikan fitur wajah(mata,hidung, dan mulut). Penelitian ini dilaksanakan secara mandiri baik pengambilan data skunder maupun proses pengolahan datanya, untuk metode pengumpulan data pada penelitian ini penulis menggunakan metode studi pustaka dan studi laboratorium. Disarankan dengan adanya penelitian ini, penulis berharap dapat memberikan kontribusi kepada peneliti yang lain untuk meneliti pengaruh-pengaruh lain yang mempengaruhi keberhasilan algoritma haar, sehingga algoritma haar dapat dikembangkan lebih baik lagi.Kata kunci: Pendektesian wajah,resolusi citra, haar like featureAbstractFace detection is one of the early stage is very important in a facial recognition system (face recognition) used in biometric identification. So far, the main obstacle in the face detection system revolves around the issue size (image resolution), the image resolution is the level of detail of an image. The higher the resolution the higher the level of detail of that image .Haar like Feature is a method commonly used in the detection of objects particularly the face detection and its features. Principle Haar-like features are simple to recognize objects based on the value of the feature but not the pixel values of the image of that object. This method has the advantage that the computation is very fast, because it depends on the number of pixels in a square instead of each pixel value of an image. To implement and analyze speed haar algorithm results in a localized facial features of this research using MATLAB R2012b software in order to know how to analyze and implement and get the results to analyze the effect of image resolution algorithm localizes haar in the facial features (eyes, nose, and mouth). This research was carried out independently both secondary data collection and processing of data, for the data collection method in this study the authors use the method of literature and laboratory studies. Suggested the presence of this study, the authors hope to contribute to fellow-researcher to examine other influences which affect the success haar algorithms, so the algorithm can be developed haar better.Keywords: face detection, image resolution, haar like feature

Facial Composite System Using Real Facial Features

2014 ◽  
Vol 22 (35) ◽  
pp. 9-15
Author(s):  
Soňa Duchovičová ◽  
Barbora Zahradníková ◽  
Peter Schreiber
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Composite System ◽  
Facial Feature ◽  
Facial Features ◽  
Feature Points ◽  
Facial Image ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Facial Composite

Abstract Facial feature points identification plays an important role in many facial image applications, like face detection, face recognition, facial expression classification, etc. This paper describes the early stages of the research in the field of evolving a facial composite, primarily the main steps of face detection and facial features extraction. Technological issues are identified and possible strategies to solve some of the problems are proposed.

scholarly journals Pre filtering techniques for face recognition based on edge detection algorithm

2017 ◽  
Vol 7 (1.1) ◽  
pp. 213
Author(s):  
Sheela Rani ◽  
Vuyyuru Tejaswi ◽  
Bonthu Rohitha ◽  
Bhimavarapu Akhil
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Detection Algorithm ◽  
Facial Features ◽  
Human Face ◽  
Video Frames ◽  
Similar Idea ◽  
The Individual ◽  
The Given ◽  
Interesting Area

Recognition of face has been turned out to be the most important and interesting area in research. A face recognition framework is a PC application that is apt for recognizing or confirming the presence of human face from a computerized picture, from the video frames etc. One of the approaches to do this is by matching the chosen facial features with the pictures in the database. It is normally utilized as a part of security frameworks and can be implemented in different biometrics, for example, unique finger impression or eye iris acknowledgment frameworks. A picture is a mix of edges. The curved line potions where the brightness of the image change intensely are known as edges. We utilize a similar idea in the field of face-detection, the force of facial colours are utilized as a consistent value. Face recognition includes examination of a picture with a database of stored faces keeping in mind the end goal to recognize the individual in the given input picture. The entire procedure covers in three phases face detection, feature extraction and recognition and different strategies are required according to the specified requirements.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

scholarly journals Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

2021 ◽  
Vol 11 (11) ◽  
pp. 68-75
Author(s):  
Enrique Lee Huamaní ◽  
◽  
Lilian Ocares Cunyarachi
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Face Detection ◽  
Face Mask ◽  
Economic Activities ◽  
Facial Mask ◽  
The Face ◽  
The Individual ◽  
A Company ◽  
Selection Of

Due to the pandemic caused by Covid-19, daily life has changed significantly. For this reason, biosecurity measures have been implemented to prevent the spread of the virus as an effective way to reactivate economic activities. In this sense, the present paper focuses on real-time face detection as a measure of control at the entrance to an entity, thus avoiding the spread of the virus while recognizing the identity of workers despite the use of masks and thus reducing the risk of entry of individuals outside the organization. Therefore, the objective is to contribute to the security of a company through the application of machine learning methodology. The selection of methodology is justified due to the adaptation of the same according to the interests of this project. Consequently, algorithms were used in a progressive manner, obtaining as a result the control system that was intended, since each particularity of the face of the individual was recognized in relation to its corresponding identification. Finally, the results of this article benefit the security of organizations regardless of their field or sector. Keywords— Control, Detection, Facial Recognition, Facial Mask, Face recognition, Machine learning.

scholarly journals Dynamic & Intelligent Student Attendance Maintenance Based on Face Recognition

2020 ◽  
Vol 9 (5) ◽  
pp. 399-403
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Face Detection ◽  
Student Attendance ◽  
Teachers And Students ◽  
The Face ◽  
Monitoring Camera ◽  
On Line

The proposed system generally results a solution to some of the problems which occurs in colleges and schools by providing a monitoring camera with the help of “Artificial Intelligence (AI)” . The main problem which can be occurred is wastage of time in taking the attendance manually or through any biometric sensors. The next problem which can be solved is to control the usage of electricity in classrooms when students are not in class. When the videos are getting recorded with the help of monitoring cameras, at the same time the head counting and face detection of the students present will also be done. When the strength of the class is zero ,the head counting also results to zero. The electricity can also be saved at the same time when people are not present in the classroom. The face recognition is the easiest process which can be done for marking the attendance, where the attendance is marked automatically. This process also helps to prevent the fake attendance. Face recognition and detection is generally based on line edge mapping to attain the identity of the student and also meets the wants of attendance in the universities and schools. The image of the student is to be captured and checked with the database simultaneously and marks the attendance of the particular student. The video gets recorded all the time and checks whether the student remains in class for the entire period.The attendance marking system with the help of technology is very essential for both the teachers and students.

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