Facial Recognition using the OpenCV Libraries of Python for the Pictures of Human Faces Wearing Face Masks during the COVID-19 Pandemic
06 Jul 2021-pp 1-5
TL;DR: In this paper, the authors used face recognition modules from python's huge collection of libraries, and trained the model to recognize people while wearing masks, since half of the facial features are lost, therefore developing a technique to recognize faces in such way is crucial.
Abstract: 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.
Citations
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24 Jun 2022
TL;DR: In this article , a model trained on the YOLOv4 algorithm was used to detect whether the target is wearing a mask in many scenes such as routine, multi-person and occlusion environment.
Abstract: 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.
1 citations
17 Mar 2023
TL;DR: In this paper , a method for facial recognition along with its implementation and applications is discussed, where the mathematical aspects of a person's face are converted into a face print, which is then stored in a database to verify an individual's identification.
Abstract: 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.
04 Dec 2022
TL;DR: In this article , 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.
Abstract: 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.
29 Apr 2023
TL;DR: In this paper , a novel approach is schemed to record attendance of all the students of a class, which uses a deep convolutional neural network face recognition algorithm which extract 128-dimensional face encodings from images and then compares these encoding 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.
Abstract: 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.
TL;DR: In this paper , a framework of methods, including detecting the shape or locations of masked faces, generating the facial expressions under masks, was proposed to optimize the merging of sub-results with useful face information such as key points of face.
Abstract: During COVID-19, people often wear masks in daily activities or communication. To solve the problem of generating faces with expressions under masks, we propose a framework of methods, including detecting the shape or locations of masked faces, generating the facial expressions under masks. Further, due to synthesizing quality facial expressions, we propose to optimize the merging of sub-results with useful face information such as key points of face. Further, we propose a framework for customization or personalization of user-preferring AI-generation results. We showed the system capable of running real-time and discussed the development in multiple aspects of research, interface, and applications.
References
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TL;DR: Wang et al. as mentioned in this paper proposed a new method for masked face recognition by integrating a cropping-based approach with the Convolutional Block Attention Module (CBAM), where the optimal cropping is explored for each case, while the CBAM module is adopted to focus on the regions around eyes.
Abstract: The global epidemic of COVID-19 makes people realize that wearing a mask is one of the most effective ways to protect ourselves from virus infections, which poses serious challenges for the existing face recognition system. To tackle the difficulties, a new method for masked face recognition is proposed by integrating a cropping-based approach with the Convolutional Block Attention Module (CBAM). The optimal cropping is explored for each case, while the CBAM module is adopted to focus on the regions around eyes. Two special application scenarios, using faces without mask for training to recognize masked faces, and using masked faces for training to recognize faces without mask, have also been studied. Comprehensive experiments on SMFRD, CISIA-Webface, AR and Extend Yela B datasets show that the proposed approach can significantly improve the performance of masked face recognition compared with other state-of-the-art approaches.
94 citations
Journal Article•
TL;DR: It was found that the impact of changes and the related ripple effect is less for AO modules compared to the Object Oriented (OO) modules, deduce that the maintainability is improved by adopting the AO methodology.
Abstract: Software developed using a proven methodology exhibits an inherent capability to readily accept the changes in its evolution. This constant phenomenon of change is managed through maintenance of software. By modelling software using Aspect Oriented Software Development (AOSD) methodology, the designer can build highly modularized software that allows changes with lesser impact compared with a non-AOSD approach. Software metrics play a vital role to indicate the degree of system inter-dependencies among the functional components and provide valuable feedback about the impact of changes on reusability, maintainability and reliability. During maintenance, software adapts to the changes in requirements and hence it is important to assess the impact of these changes across different versions of the software. This paper focuses on analysing the impact of changes towards maintenance for a set of Aspect Oriented (AO) applications taken as case study. Existing versions of three AO benchmark applications have been chosen and a set of metrics are defined to analyze the impact of changes made across different versions. An AO Software Change Impact Analyzer (AOSCIA) tool was also developed to study the impact of the changes across the selected versions. It was found that the impact of changes and the related ripple effect is less for AO modules compared to the Object Oriented (OO) modules. Hence, we deduce that the maintainability is improved by adopting the AO methodology.
9 citations
TL;DR: The new coronavirus spreads widely through droplets, aerosols and other carriers and wearing a mask can effectively reduce the probability of being infected by the virus.
Abstract: The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to...
6 citations
Posted Content•
TL;DR: In this paper, a joint evaluation and in-depth analyses of the face verification performance of human experts in comparison to state-of-the-art automatic face recognition solutions is provided. But, the study concludes with a set of take-home messages on different aspects of the correlation between the verification behavior of human and machine.
Abstract: The recent COVID-19 pandemic has increased the focus on hygienic and contactless identity verification methods. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. Recent reports have tackled this by evaluating the masked probe effect on the performance of automatic face recognition solutions. However, such solutions can fail in certain processes, leading to performing the verification task by a human expert. This work provides a joint evaluation and in-depth analyses of the face verification performance of human experts in comparison to state-of-the-art automatic face recognition solutions. This involves an extensive evaluation with 12 human experts and 4 automatic recognition solutions. The study concludes with a set of take-home messages on different aspects of the correlation between the verification behavior of human and machine.
6 citations
TL;DR: A simplified approach to serve the above purpose using the basic Machine Learning (ML) packages such as TensorFlow, Keras, OpenCV and Scikit-Learn to detect the presence of masks correctly without causing over-fitting.
Abstract: Abstract: Face mask detection involves in detection the placement of the face then crucial whether or not it's a mask thereon or not. the problem is proximately cognate to general object notion to detect the categories of objects. Face identification flatly deals with identifying a particular cluster of entities i.e., Face. it's varied applications, like autonomous driving, education, police work, and so on. This paper presents a simplified approach to serve the above purpose using the basic Machine Learning (ML) packages such as TensorFlow, Keras, OpenCV and Scikit-Learn. The planned technique detects the face from the image properly and so identifies if it's a mask on that or not. As an investigation taskperforming artist, it ought to conjointly sight a face at the side of a mask in motion. The technique perform accuracyup to 95.77% and 94.58% respectively on two different datasets and count optimized values of parameters using the Sequential Convolutional Neural Network model to detect the presence of masks correctly without causing over-fitting. Keywords: TensorFlow, Keras, OpenCV and Scikit- Learn
5 citations