This paper mainly addresses the detection of facial mask wear under the new COVID-19. To meet this demand, this paper performs facial mask wear detection on specific targets through a model trained based on the YOLOv4 algorithm. It has the characteristics of fast detection and light weight, and the application of this system to daily mask wear detection requires high real-time system performance. YOLOv4 meets this requirement, so the system designed based on this model has practical significance. This paper further demonstrates that the facial mask detection system designed based on the YOLOv4 algorithm is capable of working in multiple scenes of daily life, successfully detecting whether the target is wearing a mask in many scenes such as routine, multi-person and occlusion environment. 

Facial mask detection system based on YOLOv4 algorithm

With the extraordinary growth in images and video data sets, there is a mind-boggling want for programmed understanding and evaluation of data with the assistance of smart frameworks, since physically it is a long way off. Individuals, unlike robots, have a limited capacity to distinguish unexpected expressions. As a result, the programmed face proximity frame- work is important in face identification, appearance recognition, head-present evaluation, human-PC cooperation, and other applications. Software that uses facial recognition for face detection and identification is regarded as biometric. This study converts the mathematical aspects of a person’s face into a face print, which is then stored in a database to verify an individual’s identification. A deep learning system compares a digital image or an image taken quickly to a previously stored image(which is saved in the database). The face has a significant function in interpersonal communication for identifying oneself. Face recognition technology determines the size and placement of a human face in a digital picture. Facial recognition software has a wide range of uses in the consumer market and in the security and surveillance sectors. The COVID pandemic has brought facial recognition into greater focus lately than ever before. Face detection and recognition play a vital part in security systems that people need to interact with without making physical contact. The pattern of online exam proctoring is employing face detection and recognition. Facial recognition is used in the airline sector to enable rapid, accurate identification and verification at every stage of the passenger trip. In this research, we focused on image quality because it is the major drawback in existing algorithms and used OPEN CV, Face Recognition, and designed algorithms using libraries in python. This study discusses a method for facial recognition along with its implementation and applications.

An approach for Face Detection and Face Recognition using OpenCV and Face Recognition Libraries in Python

The proposal of a facial recognition system to increase security, through facial recognition with multiple utilities such as facilitating the access of people with adequate protection measures in times of Covid-19, as well as security when seeking to hide their identity. The methodology considers the use of tools such as Python and OpenCV, as well as models such as Eigen Faces, Fisher Faces, and LBPH Faces, as units of analysis are considered photographs and portions of the video that capture facial expressions that then their patterns are trained with facial recognition algorithms. The results obtained show that the LBPH Faces obtained confidence values lower than 70, with a 95% certainty of recognition and a shorter recognition time, improving the accuracy of facial recognition, also with the increase of the data was achieved to improve the accuracy of recognition as well as improve confidence regarding the safety of people.

Processing of Images Based on Machine Learning to Avoid Unauthorized Entry

During ancient Indian times, the Gurukul system of education was the style of learning in the country. In this system the students were learning with their mentors (Gurus) and receiving education, knowledge, moral values and life skills under the guidance of their gurus. This system of education was practiced in ancient times, where all students who resided at the place of the guru in the Gurukula were considered equal. As the days passed the things took a drastic change and government schools were introduced. A few decades ago, the number of students in government schools decreased due to the privatization of the education system. Later, more schools were introduced by corporations, which led to a significant increase in the number of schools. To perform the tasks like monitoring and taking the attendance of each class was tedious job at the same time it was time consuming, where the total number of students in each class has increased and number of subjects for each class also increased and every teacher has to document the number of students attending the classes for each subject and they need to submit it to the higher authorities. To overcome these difficulties, In this paper a novel approach is schemed to record attendance of all the students of a class. In the proposed system to take the attendance of the students all at once, a live video is processed for each frame and to recognize the faces of all the students it uses a deep convolutional neural network face recognition algorithm which extract 128-dimensional face encodings from images and then compares these encodings with the faces stored in the dataset to determine the best match and further the attendance of the students present in the class is recorded in the form of excel sheet so that the teacher can carry out the further analysis. The findings of the experiment overcomes the difficulties faced in the existing systems and eyewitnesses the furturistic transition in marking attendance.

Novel Approach to Record the Attendance of Students Using Facial Recognition

During ancient Indian times, the Gurukul system of education was the style of learning in the country. In this system the students were learning with their mentors (Gurus) and receiving education, knowledge, moral values and life skills under the guidance of their gurus. This system of education was practiced in ancient times, where all students who resided at the place of the guru in the Gurukula were considered equal. As the days passed the things took a drastic change and government schools were introduced. A few decades ago, the number of students in government schools decreased due to the privatization of the education system. Later, more schools were introduced by corporations, which led to a significant increase in the number of schools. To perform the tasks like monitoring and taking the attendance of each class was tedious job at the same time it was time consuming, where the total number of students in each class has increased and number of subjects for each class also increased and every teacher has to document the number of students attending the classes for each subject and they need to submit it to the higher authorities. To overcome these difficulties, In this paper a novel approach is schemed to record attendance of all the students of a class. In the proposed system to take the attendance of the students all at once, a live video is processed for each frame and to recognize the faces of all the students it uses a deep convolutional neural network face recognition algorithm which extract 128-dimensional face encodings from images and then compares these encodings with the faces stored in the dataset to determine the best match and further the attendance of the students present in the class is recorded in the form of excel sheet so that the teacher can carry out the further analysis. The findings of the experiment overcomes the difficulties faced in the existing systems and eyewitnesses the furturistic transition in marking attendance. 

In this research paper, we are going to see the profound scientific use of computer technology applied in the fields of AI and Machine Learning primarily focused on Image Processing and Pattern recognition. Techniques such as ours are widely used to recognize real life objects including human faces etc. Thus, using such techniques, we can recognize a person from pictures. Using face recognition modules from python's huge collection of libraries, we are able to train the model to recognize people while wearing masks. Since when masks are worn, half of the facial features are lost, therefore developing a technique to recognize faces in such way is crucial. This specific technology of face detection is used in biometrics, video surveillance, etc. Therefore it's at utmost importance to increase the security as well as efficiency whilst making the recognition faster.

Ewin Varghese

Papers

Facial Recognition using the OpenCV Libraries of Python for the Pictures of Human Faces Wearing Face Masks during the COVID-19 Pandemic