Real-time Face Recognition System Research Articles

Student Attendance Management System

The unique traces a real-time face recognition and attendance system designed to streamline attendance tracking in instructive settings. Leveraging OpenCV, face_recognition, and MongoDB, the framework mechanizes the recognizable proof of understudies from video streams or IP camera nourishes, recording their participation precisely and productively. Conventional attendance methods are regularly labor-intensive and inclined to mistakes, inciting the advancement of robotized arrangements. This investigate addresses these challenges by joining strong facial recognition algorithms with real-time video preparing capabilities and adaptable information capacity. By utilizing OpenCV for image processing, face_recognition for facial include location and acknowledgment, and MongoDB for information administration, the framework offers a comprehensive arrangement for participation following. The abstract emphasizes the system's common sense, effectiveness, and potential to revolutionize participation administration in instructive educate. Besides, it highlights the significance of exactness, preparing speed, and versatility in assessing the system's execution. Generally, the theoretical sets the organize for a detailed investigation of the system's design, execution, results, and suggestions for participation administration in educational settings.

A Novel Deep Transfer Learning-Based Approach for Face Pose Estimation

Abstract An efficient face recognition system is essential for security and authentication-based applications. However, real-time face recognition systems have a few significant concerns, including face pose orientations. In the last decade, numerous solutions have been introduced to estimate distinct face pose orientations. Nevertheless, these solutions must be adequately addressed for the three main face pose orientations: Yaw, Pitch, and Roll. This paper proposed a novel deep transfer learning-based multitasking approach for solving three integrated tasks, i.e., face detection, landmarks detection, and face pose estimation. The face pose variation vulnerability has been intensely investigated here underlying three modules: image preprocessing, feature extraction module through deep transfer learning, and regression module for estimating the face poses. The experiments are performed on the well-known benchmark dataset Annotated Faces in the Wild (AFW). We evaluate the outcomes of the experiments to reveal that our proposed approach is superior to other recently available solutions.

Design and implementation of a real-time face recognition system based on artificial intelligence techniques

Design and implementation of a real-time face recognition system based on artificial intelligence techniques

Real time face recognition system based on YOLO and InsightFace

Real time face recognition system based on YOLO and InsightFace

Reducing false positive rate with the help of scene change indicator in deep learning based real-time face recognition systems.

In face recognition systems, light direction, reflection, and emotional and physical changes on the face are some of the main factors that make recognition difficult. Researchers continue to work on deep learning-based algorithms to overcome these difficulties. It is essential to develop models that will work with high accuracy and reduce the computational cost, especially in real-time face recognition systems. Deep metric learning algorithms called representative learning are frequently preferred in this field. However, in addition to the extraction of outstanding representative features, the appropriate classification of these feature vectors is also an essential factor affecting the performance. The Scene Change Indicator (SCI) in this study is proposed to reduce or eliminate false recognition rates in sliding windows with a deep metric learning model. This model detects the blocks where the scene does not change and tries to identify the comparison threshold value used in the classifier stage with a new value more precisely. Increasing the sensitivity ratio across the unchanging scene blocks allows for fewer comparisons among the samples in the database. The model proposed in the experimental study reached 99.25% accuracy and 99.28% F-1 score values ​​compared to the original deep metric learning model. Experimental results show that even if there are differences in facial images of the same person in unchanging scenes, misrecognition can be minimized because the sample area being compared is narrowed.

Smart Attendance System Using OpenCV

-The essential part of the human body that distinguishes an individual is their face. The primary objective of this project is to develop an attendance monitoring system based on facial recognition for academic system in order to upgrade and improve the current attendance management system and increase its effectiveness and efficient than it existed previously. With an OpenCV Real-time Face Recognition based attendance system, you may not only capture attendances but also provide security when the system needs to identify a specific student’s identity and the moment of their admission. Since it is entirely automated and much simpler, it is simple to handle the records that will be transformed into an excel file and to keep track of the students or visitors who are present. The goal of this study was to use OpenCV to create a Real-time face recognition system for school attendance that would gather information about students who were present before or after class. The system will need student registration and a password, automatically record each student’s attendance using face recognition, and transform that data into an excel or text file. Key Words: OpenCV, Real-time face recognition

Design of Boosted Cascade and RTLBP Features based Face Recognition Module Using ML in OpenCV

Over the past ten years, face recognition in images has become more and more important. Since facial recognition is a natural talent for humans, it could be challenging to train a computer to recognize faces. The detecting difficulty comprises three stages. Without the requirement for picture difference or skin color identification, the detector operates in real-time at fifteen frames per second. Proposed and developed is a face recognition system based on Real Time Local Binary Pattern (RTLBP) and Boosted Cascade of Simple Features (BCSF). Preprocessing, face identification and recognition, and annotation are the three stages of the system. Segmentation, filtering, thresholding, and improving image quality are all part of preprocessing. The face, which is crucial for identification, is identified using the BCSF approach. Every face in the database has a correlation and RTLBP calculation. Correlation and RTLBP analysis between the captured face and the database face is then used to conduct recognition. One statistical technique that falls within the large category of factor analysis is the Boosted Cascade of Simple Features. By reducing the massive quantity of data stored to the extent of the feature space needed to represent the data efficiently, the BCSF seeks to achieve this goal. The RTLBP is designed to recognize faces by using a wide 1-D pixel vector consisting of a 2-D face picture in compact main parts of the space function. This is referred to as self-space projection. The identity of the vectors in the covariance matrix, which is centred on a set of fingerprint images, helps determine the proper space. Using OpenCV, Open Computer Vision (OpenCV), Python, Eigen-face, Fisher Face, LBPH, and Haar Cascade programming, an algorithm for a camera-based, real-time face recognition system is developed in this project. The name of the detected individual appears in the top left corner of a highlighted rectangle that annotates the face. The process of recognition takes 10.18 seconds, with a 99.93% success rate.

Advancements in Real-Time Face Recognition Algorithms for Enhanced Smart Video Surveillance

Smart video surveillance systems are essential for guaranteeing security in a variety of settings. The capacity to recognize and follow people of interest inside video feeds is made possible by real-time face recognition, which is a key component of these systems. The refinement of a real-time face recognition system designed for video surveillance applications is the focus of this research. We proposed a technique that can effectively recognize faces in a variety of lighting situations, poses, and occlusions by utilizing innovative combination of Haar Cascade and Convolutional Neural Networks. To balance computational difficulties and increase scalability, the merging of edge computing and cloud-based solutions are also investigated. The performance of the proposed system is evaluated by vigilant evaluation, demonstrating its applicability in real-world circumstances. Our research aims to advance video surveillance technology with an emphasis on the synergy of real-time face recognition for more secure and effective surveillance systems.

Optimizing Face Recognition Inference with a Collaborative Edge–Cloud Network

The rapid development of deep-learning-based edge artificial intelligence applications and their data-driven nature has led to several research issues. One key issue is the collaboration of the edge and cloud to optimize such applications by increasing inference speed and reducing latency. Some researchers have focused on simulations that verify that a collaborative edge-cloud network would be optimal, but the real-world implementation is not considered. Most researchers focus on the accuracy of the detection and recognition algorithm but not the inference speed in actual deployment. Others have implemented such networks with minimal pressure on the cloud node, thus defeating the purpose of an edge-cloud collaboration. In this study, we propose a method to increase inference speed and reduce latency by implementing a real-time face recognition system in which all face detection tasks are handled on the edge device and by forwarding cropped face images that are significantly smaller than the whole video frame, while face recognition tasks are processed at the cloud. In this system, both devices communicate using the TCP/IP protocol of wireless communication. Our experiment is executed using a Jetson Nano GPU board and a PC as the cloud. This framework is studied in terms of the frame-per-second (FPS) rate. We further compare our framework using two scenarios in which face detection and recognition tasks are deployed on the (1) edge and (2) cloud. The experimental results show that combining the edge and cloud is an effective way to accelerate the inferencing process because the maximum FPS achieved by the edge-cloud deployment was 1.91× more than the cloud deployment and 8.5× more than the edge deployment.

Real time face recognization attendance management system

Real Time face recognition system which is worked on the scenario of recognition of face detection and further also we used as it for attendance marking purpose. Marking attendance in the classroom or in any institution level during ongoing period of time is not only headache activity but also a time consuming. Due to an unpredictable or an unusually high number of students which are present during the ongoing lecture so there will be high chance of probability of proxy’s attendance if the guide did not connect the class so there are highly chances. Attendance marking system with conventional types of method has been an area of challenges occurs. Technology plays a significant part in today's society. One of the most extensively utilized technologies is face recognition. It can be used for security, authentication, and identity, as well as unlocking phones. It is extensively utilized, despite having low accuracy when compared to iris detection and fingerprint identification. Furthermore, this technology is employed in a variety of settings, including schools, colleges, hospitals, and a variety of others.

Student Attendance Analysis and Statistics Platform based on Capture Recognition Technology

With the development of face recognition technology and HD camera, it is possible to use face recognition to realize the classroom attendance statistics. The traditional way of classroom attendance statics needs teachers to roll call according to the list of students, but face recognition can not only save the time of class, but also lighten the burden of statistics attendance of school. This paper realizes the face recognition system of attendance analysis and statistics platform, and it needs cameras and a main computer. In software, under the development environment of VS2017, it relies on OPENCV, arcsoft face recognition SDK and My -SQL database realizes a real-time video stream face recognition system with C++ programming. The system captures the student face by the camera installed in the classroom, then using SDK extracts the face feature and compares with the feature in the database. When the compare value is over the set value, the system will output the corresponding students' information and completion time to the TXT file.

Face Recognition Based Automated Attendance Management System

At the beginning and end of each session, attendance is an important aspect of the daily classroom evaluation. When using traditional methods such as calling out roll calls or taking a student's signature, managing attendance can be a time-consuming task. The teacher normally checks it, although it's possible that a teacher will miss someone or some students' answers many times. Face recognition-based attendance system is a solution to the problem of recognizing faces for the purpose of collecting attendance by utilizing face recognition technology based on high-definition monitor video and other information technology. Instead of depending on time-consuming approaches, we present a real-time Face Recognition System for tracking student attendance in class in this work. The suggested method included identifying human faces from a webcam using the Viola-Jones technique, resizing the identified face to the desired size, and then processing the resized face using a basic Local Binary Patterns Histogram algorithm. After the recognition is completed, the attendance will be immediately updated in a SQLite database with the relevant information. Many institutions will profit greatly from this endeavor. As a result, the amount of time it takes and the number of human errors it makes are minimized, making it more efficient.

Face Recognition System Based on Four State Hidden Markov Model

Computational complexity is a matter of great concern in real time face recognition systems. In this paper, four state hidden Markov model for face recognition has been presented whereby coefficients of feature vectors have been curtailed. Face images have been divided into a sequence of overlapping blocks. An observation sequence containing coefficients of eigen values and eigenvectors of these blocks have been used to train the model and each subject is associated with a separate hidden Markov model. The computational complexity of the proposed model has been minimized by employing discrete wavelet transform in the preprocessing stage. Furthermore, singular value decomposition has been employed on face images and a threshold singular value is determined empirically to reject or accept test images. Principal component analysis is used for feature extraction. Accepted test images are classified based on the majority vote criteria using different observation sequences of image features. Experimental findings on Yale and ORL databases in noisy such as Salt and Pepper and noise free environments reveal that the recognition accuracy of the proposed model is comparable to the existing techniques with reduced computational cost.

Human rights during the mass introduction of artificial intelligence and robotic systems into public life

Purpose: This article considers legal approaches to implementing human rights during the mass exploitation of artificial intelligence and robotic systems in public life. Methods: Within the framework of this study, an emphasis is placed on the legal regulation of artificial intelligence systems and robotics used for remote biometric identification of a person and the creation of social credit systems. This study analyzes different models of legal regulation that are typical of certain countries and regions, including the UK, USA, China, and the EU. Results: In the UK, it is allowed to use real-time face recognition systems in public spaces but the set of scenarios and situations for their use is significantly limited by legislation and law enforcement. The legal regulation of these systems in each state is based on a constant dialogue between state and civil society. The use of artificial intelligence and robotic systems to create social credit systems is tested in some countries. Modern states have formed several approaches to the creation of such systems: some of them completely prohibit these systems, while others develop a technological and regulatory framework for the creation of national systems.

High-Capacity Real-Time Face Retrieval Recognition Algorithm Based on Task Scheduling Model for the Treatment Area of Hospital

This paper presents an in-depth study of face detection, face feature extraction, and face classification from three important components of a high-capacity face recognition system for the treatment area of hospital and a study of a high-capacity real-time face retrieval and recognition algorithm for the treatment area of hospital based on a task scheduling model. Considering the real-time nature of our system, our face feature extraction network is modeled by DeepID, and the network is slightly improved by introducing a central loss verification signal to train a DeepID-like network model using central loss and use it to extract face features. To further investigate and optimize the schedulability analysis problem of the directed graph real-time task model, this paper proposes a rigorous and approximate response time analysis method for the directed graph real-time task model with an arbitrary time frame. Based on the theoretical results of the greatly additive algebra, it is shown that the coherent qualifying function is linearly periodic, i.e., the function can be represented by a finite nonperiodic part and an infinitely repeated periodic part, thus calculating the coherent qualifying function independent of the magnitude of the interval time. The algorithm for high-capacity real-time face retrieval and recognition in the treatment area of hospital based on the task scheduling model is further investigated, and a face database is established by using the PCA dimensionality reduction technique. Based on the internal architecture of the processor, image preprocessing and IP core packaging are implemented, and the hardware engineering of the high-capacity real-time face recognition system for hospital visits is built using the IP-based design concept. The performance tests of the face detection model and feature extraction network show that the face detection model has a significant reduction in false-positive rate, better fitting of border regression, and improved time performance. The face feature extraction network has no overfitting, and the features are highly discriminative with small feature extraction time consumption. The high-capacity real-time face recognition system for the treatment area of hospital combined with the optimized directed graph task scheduling model can approach 25 fps, which meets the real-time requirements, and the face recognition rate surpasses that of real people. It realizes the intelligence, self-help, and autonomy of medical services and satisfies the medical needs of users in all aspects.

CFSM: a novel frame analyzing mechanism for real-time face recognition system on the embedded system

CFSM: a novel frame analyzing mechanism for real-time face recognition system on the embedded system

A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODEL

. In the field of deep learning, facial recognition belongs to the computer vision category. In various applications such as access control system, security, attendance management etc., it has been widely used for authentication and identification purposes. In deep learning, transfer learning is a method of using a neural network model that is first trained on a problem similar to the problem that is being solved. The most commonly used face recognition methods are mainly based on template matching, geometric features based, algebraic and deep learning method. The advantage of template matching is that it is easy to implement, and the disadvantage is that it is difficult to deal with the pose and scale changes effectively. The most important issue, regardless of the method used in the face recognition system, is dimensionality and computational complexity, especially when operating on large databases. In this paper, we applied a transfer learning model based on AlexNet Deep convolutional network to develop a real time face recognition system that has a good robustness to face pose and illumination, reduce dimensionality, complexity and improved recognition accuracy. The system has a recognition accuracy of 98.95 %.

범죄예방을 위한 CCTV 영상 기반의 실시간 안면인식 시스템

현대 사회에서 성범죄율은 지속적으로 증가하고 있다. 특히 성범죄자의 경우 재범률이 높기 때문에 재발을 막기 위한 제도적 혹은 사회적 필요성이 점차 증가하고 있다. 이를 위해 본 논문에서 범죄예방을 위한 CCTV 영상 기반의 실시간 범죄자 안면 인식 시스템을 제안한다. 이는 인공지능 기반의 안면인식을 통해 성범죄 예방 및 성범죄자의 재범 방지를 위한 일련의 프로세스다. 하지만, 안면 인식 기술을 실질적으로 활용하기에는 문제점이 있다. 안면 인식 시스템은 영상에서 안면을 찾는 탐지 단계와 탐지된 안면과의 일치도를 계산하는 매칭 단계로 이루어지며 단일 작업에 비해 큰 연산 비용이 요구된다. 때문에 본 연구에서는 안면을 탐지할 때 벡터화 작업을 통해 처리 시간의 소모를 줄여 실시간 작업 시에 연산 비용을 줄이고, 이후 탐지된 안면 영상의 크기를 늘리는 AB-box 방식을 사용해 더 높은 인식 정확도를 보인다.

Efficient Face Recognition System for Operating in Unconstrained Environments.

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.

An interactive robot design to find missing people and inform their location by real-time face recognition system on moving images

An interactive robot design to find missing people and inform their location by real-time face recognition system on moving images