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

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. © 2023 IEEE.

Framework for biometric iris recognition in video, by deep learning and quality assessment of the iris-pupil region

In the current world scenario the influence of the COVID19 pandemic has reached universal proportions affecting almost all countries. In this sense, the need has arisen to wear gloves or to reduce direct contact with objects (such as sensors for capturing fingerprints or palm prints) as a sanitary measure to protect against the virus. In this new reality, it is necessary to have a biometric identification method that allows safe and rapid recognition of people at borders, or in quarantine controls, or in access to places of high biological risk, among others. In this scenario, iris biometric recognition has reached increasing relevance. This biometric modality avoids all the aforementioned inconveniences with proven high efficiency. However, there are still problems associated with the iris capturing and segmentation in real time that could affect the effectiveness of a System of this nature and that it is necessary to take into account. This work presents a framework for real time iris detection and segmentation in video as part of a biometric recognition system. Our proposal focuses on the stages of image capture, iris detection and segmentation in RGB video frames under controlled conditions (conditions of border and access controls, where people collaborate in the recognition process). The proposed framework is based on the direct detection of the iris-pupil region using the YOLO network, the evaluation of its quality and the semantic segmentation of iris by a Fully Convolutional Network. (FCN). The proposal of an evaluation step of the quality of the iris-pupil region reduce the passage to the system of images with problems of out of focus, blurring, occlusions, light changing and pose of the subject. For the evaluation of image quality, we propose a measure that combines parameters defined in ISO/IEC 19794-6 2005 and others derived from the systematization of the knowledge of the specialized literature. The experiments carried out in four different reference databases and an own video data set demonstrates the feasibility of its application under controlled conditions of border and access controls. The achieved results exceed or equal state-of-the-art methods under these working conditions.

Covid-19 Control: Face Mask Detection Using Deep Learning for Balanced and Unbalanced Dataset

Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. This paper compares three state-of-the- art object detection approaches: Haarcascade, Multi-task Cascaded Convolutional Networks (MTCNN), and You Only Look Once version 4 (YOLOv4) to classify who is wearing a mask, who is not wearing a mask, and most importantly, who is incorrectly wearing the mask in a real-time video stream using FPS as a benchmark to select the best model. Yolov4 got about 40 Frame Per Seconds (FPS), outperforming Haarcascade with 16 and MTCNN with 1.4. YOLOv4 was later used to compare the two datasets using Intersection over Union (IoU) and mean Average Precision (mAP) as a comparative measure;dataset2 (balanced dataset) performed better than dataset1 (unbalanced dataset). Yolov4 model on dataset2 mapped and detected images of masks worn incorrectly with one correct class label rather than giving them two label classes with uncertainty in dataset1, this work shows the advantage of having a balanced dataset for accuracy. This work would help decrease human interference in enforcing the COVID-19 face mask rules and create awareness for people who do not comply with the facemask policy of wearing it correctly. Hence, significantly reducing the spread of COVID-19.

Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs

The adoption of video conferencing and video communication services, accelerated by COVID-19, has driven a rapid increase in video data traffic. The demand for higher resolutions and quality, the need for immersive video formats, and the newest, more complex video codecs increase the energy consumption in data centers and display devices. In this paper, we explore and compare the energy consumption across optimized state-of-the-art video codecs, SVT-AV1, VVenC/VVdeC, VP9, and x.265. Furthermore, we align the energy usage with various objective quality metrics and the compression performance for a set of video sequences across different resolutions. The results indicate that from the tested codecs and configurations, SVTAV1 provides the best tradeoff between energy consumption and quality. The reported results aim to serve as a guide towards sustainable video streaming while not compromising the quality of experience of the end user. © 2022 IEEE.

Techniques for Detecting Video Shot Boundaries: A Review

Due to the Corona Virus Diseases (COVID-19) pandemic, education is completely dependent on digital platforms, so recent advances in technology have made a tremendous amount of video content available. Due to the huge amount of video content, content-based information retrieval has become more and more important. Video content retrieval, just like information retrieval, requires some pre-processing such as indexing, key frame selection, and, most importantly, accurate detection of video shots. This gives the way for video information to be stored in a manner that will allow easy access. Video processing plays a vital role in many large applications. The applications required to perform the various manipulations on video streams (as on frames or say shots). The high definition of video can take a lot of memory to store, so compression techniques are huge in demand. Also, object tracking or object identification is an area where much considerable research has taken place and it is in progress.

Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining

Rich temporal information and variations in viewpoints make video data an attractive choice for learning image representations using unsupervised contrastive learning (UCL) techniques. State-of-the-art (SOTA) contrastive learning techniques consider frames within a video as positives in the embedding space, whereas the frames from other videos are considered negatives. We observe that unlike multiple views of an object in natural scene videos, an Ultrasound (US) video captures different 2D slices of an organ. Hence, there is almost no similarity between the temporally distant frames of even the same US video. In this paper we propose to instead utilize such frames as hard negatives. We advocate mining both intra-video and cross-video negatives in a hardness-sensitive negative mining curriculum in a UCL framework to learn rich image representations. We deploy our framework to learn the representations of Gallbladder (GB) malignancy from US videos. We also construct the first large-scale US video dataset containing 64 videos and 15,800 frames for learning GB representations. We show that the standard ResNet50 backbone trained with our framework improves the accuracy of models pretrained with SOTA UCL techniques as well as supervised pretrained models on ImageNet for the GB malignancy detection task by 2–6%. We further validate the generalizability of our method on a publicly available lung US image dataset of COVID-19 pathologies and show an improvement of 1.5% compared to SOTA. Source code, dataset, and models are available at https://gbc-iitd.github.io/usucl. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

YouTube Videos as Data: Seeing Daily Challenges for People with Visual Impairments during COVID-19

COVID-19 pandemic has spread globally and affected a large number of people. Previous studies collected online survey and interview data to raise awareness of requirements from people with visual impairments (PVI) under the COVID-19 pandemic, however, little has been observed in PVI's daily activities due to the suspension of face-to-face fieldwork. In this study, we utilized an innovative data source-YouTube videos to fill the vacancy of observation data in this specific topic. Compared to previous studies, we got more voices involved and gained a richer dataset by considering both videos from the visually impaired community where PVI are primary authors and news media videos where PVI are involved as active participants. Eventually, we collected 24 videos created by the visually impaired community and 27 videos from the news media community, where 57 PVI were depicted. This study uncovered the problems causing pandemic-related challenges and suggested the need for explicit guidelines that can make the prevention protocols accessible and inclusive for PVI, as this study indicates that accessibility can be easily missed under unforeseen situations like the COVID-19 pandemic. © 2022 ACM.

Face Mask Detection with Temperature Check

The coronavirus (COVID-19) pandemic is causing a worldwide health catastrophe, so according to the World Health Organization (WHO), wearing masks in public is an effective safety method. The COVID-19 pandemic has forced governments around the world to impose quarantines to prevent transmission of the virus. According to reports, wearing masks in public does reduce the threat of transmission of the virus. An efficient and cost-effective way to use Artificial Intelligence (AI) to create a secure environment in a manufacturing environment. A hybrid model for using a deep and classic face mask detection device will be proposed. The face mask detection dataset includes the mask, and without mask photos, it uses the Open-Source Computer Vision Library (OpenCV) to detect faces in real-time from the stay circulation through the webcam. It uses the dataset to build a computer vision COVID-19 face mask detector using Python, OpenCV, TensorFlow, and Keras. Using computer vision and deep learning, the goal is to understand whether a character in a picture or video stream is wearing a mask or not using computer vision and deep learning.

TOURISM, NATURAL PROTECTED AREAS AND OPEN SOURCE GEOSPATIAL TECHNOLOGIES

The Covid-19 outbreak has greatly impacted society behaviours fostering proximity tourism and valorising the social role of peri-urban natural protected areas as key locations for outdoor activities. FOSS and FOSS4G can play a critical role to support the value creation for these sites. This work evaluates its application in the context of two different protected areas for the creation of 3D digital products, the monitoring of touristic fluxes and the conduction of parks management activities. To this aim three solutions that copes with the mentioned aspects are presented and gaps, weakness and limitations evaluated. The investigated solutions consists in: the data workflow from survey to 3D rendering using Blender and GIS plugin;the touristic fluxes monitoring system based on a machine learning algorithm for image recognition from captured video data streams and istSOS;and finally the park assets management system which is based on PostGIS and OpenLayers.

CNN for Hand Washing Movement Classification: What Matters More - the Approach or the Dataset?

Good hand hygiene is one of the key factors in preventing infectious diseases, including COVID-19. Advances in machine learning have enabled automated hand hygiene evaluation, with research papers reporting highly accurate hand washing movement classification from video data. However, existing studies typically use datasets collected in lab conditions. In this paper, we apply state-of-the-art techniques such as MobileNetV2 based CNN, including two-stream and recurrent CNN, to three different datasets: a good-quality and uniform lab-based dataset, a more diverse lab-based dataset, and a large-scale real-life dataset collected in a hospital. The results show that while many of the approaches show good accuracy on the first dataset, the accuracy drops significantly o n t he m ore complex datasets. Moreover, all approaches fail to generalize on the third dataset, and only show slightly-better-than random accuracy on videos held out from the training set. This suggests that despite the high accuracy routinely reported in the research literature, the transition to real-world applications for hand washing quality monitoring is not going to be straightforward. © 2022 IEEE.

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and running the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical challenges. [ FROM AUTHOR] Copyright of Sociological Methods & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting

With the increasing number of video applications, it is essential to resolve issues such as ineffective search of video content, tampered/forged video content, packet loss, to name a few. Data embedding is typically utilized as one of the solutions to address the aforementioned issues. One of the important requirements of data embedding is to maximize embedding capacity with minimal bit rate overhead while ensuring imperceptibility of the inserted data. However, embedding capacity varies depending on the video content and increasing the embedding capacity usually leads to video quality degradation. In this work, a threshold-controlled block splitting technique is proposed for embedding data into SHVC video. Specifically, the embedding capacity can be increased by coding the host video by using more small blocks, which can be achieved by tuning a threshold-controlled parameter in the rate distortion optimization process. Subsequently, the predictive syntax elements in both intra and inter-coded blocks are jointly utilized to embed data, which ensures that data can be embedded regardless of the prediction mode used in coding a block. Results suggest that the proposed method can achieve a trade-off between the increase in embedding capacity and bit rate overhead while maintaining video quality. In the best case scenario, the sequence PartyScene can embed 516.9 kbps with an average bit rate overhead of +7.0% for the Low Delay P configuration, while the same video can embed 1578.6 kbps with an average bit rate overhead of +2.9% for the All Intra configuration.

Development of Real-time Handwashing Assessment using Video Analysis

The world communities have suffered from the COVID-19 pandemic for the last two years. Even though many countries have started to normalise the situation, the COVID-19 still becomes a severe threat in the future. Healthy habits, such as complete and frequent handwashing, still need to be practised. These habits can minimise the transmission risks. The paper proposed a single-board computer system that aims to assess the handwashing steps. The standardised handwashing procedure is used to validate the acquired video of hand movement. The system is installed in a Raspberry Pi and receives video data from the connected mini camera. The deep learning model is implemented to provide classification capabilities. The assessment result is summarised according to the movement completeness and total duration. The testing stages found that the proposed system can provide accuracy and F1-score values of 82.55% and 86.66%, respectively. © 2021 ACM.

Comparing Technical Trends between Industrial Leaders via Video Data

IT (Information technology) has been rapidly growing. Until early 2000s, IT mainly exists for IT industry. However, IT expands their fields into the outer fields of IT industry such as medical and agricultural industries. It means that the cutting-edge technologies have increased to a level that humanity cannot grasp all. It is difficult for even industrial leaders and followers to grasp all. Thus, in order to grasp and create the cutting-edge technologies, this research provide the latest states from keynotes of some events. These text data for this analysis could be gained because a lot of events have shifted from in-person to online by the impact of COVID-19 (the coronavirus disease 2019). Consequently, this analysis measured the closeness between industries that have available data and found that the changes of topics become more frequent after COVID-19. This analysis is to evaluate the potential in order to compare with confidential data and to discover the gap between international trends and in-company competences in the future. © 2021 IEEE.

Engage AI: Leveraging Video Analytics for Instructor-class Awareness in Virtual Classroom Settings

A difficulty for teachers in COVID-era online teaching settings is assessing engagement and student attention. This has made adapting teaching to the responses of the class a challenge. We developed a system called Engage AI for assessing engagement during live lectures. Engage AI uses video-based machine learning models to detect drowsiness and emotions like happiness and neutrality, and aggregates them in a dashboard that instructors can view as they speak. This provides real-time feedback to instructors, allowing them to adjust their teaching to keep students engaged. There is no video data transmitted outside of students' web browsers, and individual students are anonymous to the instructor. Testing in undergraduate engineering lectures resulted in 78.2% reporting feeling at least potentially more engaged during the lecture and at least 34.4% of students reporting feeling more engaged during the lecture. These approaches could be applicable to many forms of remote and in-person education. © American Society for Engineering Education, 2021