Real-time Face Recognition Research Articles

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 %.

Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR

This study shows an enhancement of IoT which gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any potential misfortune in the environment. Sensor data is pushed onto the local system and GoDaddy Cloud, whenever the camera detects a person to optimize the Physical Location Monitoring System by reducing the bandwidth requirement and storage cost onto the Cloud using edge computation. The study reveals that Decision Tree (DT) and Random Forest give reasonably similar macro average f1-score to predict a person using sensor data. Experimental results show that DT is the most reliable predictive model for the Cloud datasets of three different physical locations to predict a person using timestamp with an accuracy of 83.99%, 88.92%, and 80.97%. This study also explains multivariate time series prediction using Vector Auto Regression that gives reasonably good Root Mean Squared Error to predict Temperature, Humidity, Light Dependent Resistor, and Gas time series.

SyncBIM: The Decision-Making BIM-Based Cloud Platform with Real-time Facial Recognition and Data Visualization

SyncBIM: The Decision-Making BIM-Based Cloud Platform with Real-time Facial Recognition and Data Visualization

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.

Happy Cow or Thinking Pig? WUR Wolf—Facial Coding Platform for Measuring Emotions in Farm Animals

Emotions play an indicative and informative role in the investigation of farm animal behaviors. Systems that respond and can measure emotions provide a natural user interface in enabling the digitalization of animal welfare platforms. The faces of farm animals can be one of the richest channels for expressing emotions. WUR Wolf (Wageningen University & Research: Wolf Mascot), a real-time facial recognition platform that can automatically code the emotions of farm animals, is presented in this study. The developed Python-based algorithms detect and track the facial features of cows and pigs, analyze the appearance, ear postures, and eye white regions, and correlate these with the mental/emotional states of the farm animals. The system is trained on a dataset of facial features of images of farm animals collected in over six farms and has been optimized to operate with an average accuracy of 85%. From these, the emotional states of animals in real time are determined. The software detects 13 facial actions and an inferred nine emotional states, including whether the animal is aggressive, calm, or neutral. A real-time emotion recognition system based on YoloV3, a Faster YoloV4-based facial detection platform and an ensemble Convolutional Neural Networks (RCNN) is presented. Detecting facial features of farm animals simultaneously in real time enables many new interfaces for automated decision-making tools for livestock farmers. Emotion sensing offers a vast potential for improving animal welfare and animal–human interactions.

Research on Face Recognition Based on Improved Convolutional Neural Network Using Raspberry Pi

In this paper, a face recognition system has been designed using Raspberry Pi based on convolution neural network technology. Convolution neural network that using OpenCV (open source computer vision library) to realize real-time face recognition and feature extraction. The difficult and important problem in deep learning network is that it could be over fitting very easily. In order to alleviate the over fitting phenomenon and improve the accuracy of face recognition, the dropout technology of convolutional neural network had been employed to improve the recognition accuracy and ensure the robustness of face recognition. The results of experiments show that our methods has the advantages in the computer time and recognition rate for the low construction cost, simple structure and strong portability. The accuracy of face detection for static and dynamic images using Raspberry Pi is above 85% in our testament.

Smart drone with real time face recognition

This paper develops a solution to help the task force using a face recognition based Unmanned Aerial Vehicle (UAV) to identify the criminals, missing people, civilians and for surveillance. A facial recognition system is a technology which involves the understanding of how the faces are detected and recognized, usually employed to authenticate users through Identity verification services and computing facial features from a given image. This work elucidates an unmanned aerial vehicle with a camera attached that is linked to Face Recognition software and used to control a robot through a wireless remote. When the drone's camera angle is within 37 degrees, the accuracy is 98.6%, indicating that the suggested method can handle changing drone placements. Some applications are to assist the police officials with their investigations like search and rescue, investigations of incidents, crime analysis and crowd monitoring.

Digital Media Application Technology of Mobile Terminals Based on Edge Computing and Virtual Reality

With the maturity of the most advanced technology and virtual reality, the application of digital media technology in various fields has become more and more extensive. Digital media technology has greatly promoted the development of all classes of society. Therefore, how to develop digital media technology applications has become a major issue. This article aims to study the digital media application technology of mobile terminals based on edge computing and virtual reality. In this paper, we use edge computing and virtual reality methods to study the digital media application technology of mobile terminals. We used the SD-CEN architecture and FWA in edge computing to conduct simulation experiments and compared the delay time performance of the FWA with that of the PSO-CO algorithm, WRR algorithm, and Pick-KX algorithm. The results of this paper show that it can effectively reduce the response delay of real-time face recognition services and improve user experience. Compared with the traditional cloud computing architecture and a single MEC device, the SD-CEN network architecture based on FWA strategy has more advantages. The latency of the FWA is increased by 61%, 46%, and 17%, respectively, compared with that of the WRR, Pick-KX, and PSO-CO algorithms.

Exam form automation using facial recognition

The way Data Science and Machine Learning have set modern trends for automation. It is thoughtful to simplify our day to day activities which are related to these domains. One such task is the repetitive submission of the same personal details while submitting any online form regarding academic or other purposes. The repetitiveness seems a bit dilatory. Plus, the involvement of machine learning further espouses the concept to automate re-iteration of personal details each time we fill a form, instead of manual entry.To obviate the above redundancy, we have proposed to implement “Exam Form Automation Using Facial Recognition” which includes real time face recognition with the help of webcam, pre-storing data on web servers with Panda and NumPy library of Python and then automating the entries on form using selenium library of python. The system captures a real time image of a user and collects the particular details from data hosted on a web server. Thus, precluding the monotonous procedure of manual entry.

Real Time Implementation of Face Recognition based Smart Attendance System

This research work addresses the issue of incorporating an automatic attendance system to the frame of an institution using face detection and recognition techniques. The proposed system aims at reducing computational time with available hardware to yield more efficient results. The proposed model utilizes Histogram Oriented Gradients and facial encodings derived from facial landmarks. It also addresses the problems related to accuracy of facial recognition and the resource requirement for quick, real-time facial recognition by applying multi-processing. The improvement in performance in terms of accuracy across two different methods, and the improvement in terms of time requirement for the same method using different strategies have also been documented for demonstration. The designed system demonstrates the effectiveness of task parallelization with a minimum amount of hardware desiderata. The system has been designed to an optimum self-sustaining ecosystem which can efficiently operate on its own accord and compute comprehensible feedback without the requirement of any third-party human interference. A Graphical User Interface has been incorporated into the system for maximum user comprehensibility

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

The design, implementation and pilot application of an intelligent online proctoring system for online exams

PurposeThe purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic university online exam and checked the proctoring result.Design/methodology/approachThe IOPS adopts the B/S (Browser/Server) architecture. The server side is implemented with programming language C and Python and stores the identification data of all examinees and their important behavior change status, including facial expression, eye and mouth movement and speech. The browser side collects and analyzes multimodal data of the examinee writing the online test locally and transfers the examinee’s most important behavior status change data to the server. Real-time face recognition and voice detection are implemented with the support of open-source software.FindingsThe system was integrated into a Web-based intelligent tutoring system for school mathematics education. As a pilot application, the system was also used for online proctored exam in an undergraduate seminar in Peking University during the epidemic period in 2020. The recorded log data show that all students concentrated themselves on the exam and did not leave the camera and did not speak.Originality/valueDuring the epidemic period of the novel coronavirus outbreak, almost all educational institutions in the world use online learning as the best way to maintain the teaching and learning schedule for all students. However, current online instruction platforms lack the function to prevent the learners from cheating in online exams and cannot guarantee the integrity and equality for all examinees as in traditional classroom exams. The literature review shows that the online proctoring system should become an important component of online exams to tackle the growing online cheating problem. Although such proctoring systems have been developed and put on the market, the practical usage of such systems in authentic exams and its effect have not been reported. Those systems are heavyweight and commercial product and cannot be freely used in education. The light-weight IOPS developed by the authors could meet the need for online exam as a stable and practical approach and could contribute to the growing online learning and distance learning.

Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

Face Recognition Using Face Embedding Method

The recognition of faces is of great significance for real worlds applications like video surveillance, classroom students attendance recording, human-machine interaction, security systems etc. It is still several challenges to detection and recognition for recognising multiple faces because it is not easy for detecting multiple faces in one frame, it is also hard to recognise faces with low resolution. Compared to machine learning traditional approaches, approaches based-on deep learning shown better performance in terms of processing speed and accuracy in face recognition. Our method uses a combination of Viola Jones, Face-net and Support Vector Machine (SVM), and achieved an accuracy of 94% for 100 person through 166s and for real time face recognition, it achieved an accuracy of 100% for 10 frames through 9 seconds.

Neural Networks (CNNs) and Vgg on Real Time Face Recognition System

Face Recognition is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human's faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along with classifiers are used for the recognition of faces. This work introduces a new real time face Recognition system. The process is broken into four major steps: (1) database collection, (2) face recognition to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92.Second step, efficient real time face recognition algorithm is then used to recognize faces with a known database. For real time face Recognition, VGG-16 with Transfer Learning and Convolutional Neural Network (CNN) are used. This proposed system is implemented using keras. Lastly the performance of these two classifiers is measured using of precision, recall, F1-score, and accuracy.

Face recognition based automated student attendance system

Face recognition technologies have gone so far in the modern world. The usage of real-time facial recognition is an efficient solution which improves the student attendance system. Previously considered inaccurate and unfinished, face recognition, driven by progress in Deep Learning, is now asserting itself as one of the most advanced biometric identification technology. Thus, many universities around the world integrate it into their teaching platforms. Face recognition is an open door to distance education too, so these programs are on the rise. It helps in conversion of the frames of the video into images so that the face of the student can be easily recognized for their attendance so that the attendance database can be easily reflected automatically

A FACIAL RECOGNITION USING OPEN COMPUTER VISION

The Face recognition and image or video recognition are popular research topics in biometric technology. Real-time face recognition is an exciting field and a rapidly evolving issue. Key component analysis (PCA) may be a statistical technique collectively called correlational analysis . The goal of PCA is to scale back the massive amount of knowledge storage to the dimensions of the functional space required to render the face recognition system. The wide one-dimensional pixel vector generated from the two-dimensional image of the face and therefore the basic elements of the spatial function are designed for face recognition using PCA. this is often the projection of your own space. Sufficient space is decided by the brand. specialise in the eigenvectors of the covariance matrix of the fingerprint image collection. i'm building a camera-based real-time face recognition system and installing an algorithm. Use OpenCV, Haar Cascade, Eigen face, Fisher Face, LBPH and Python for program development.

Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.

Multi-face Recognition for the Detection of Prisoners in Jail using a Modified Cascade Classifier and CNN

Identifying the identity of a prisoner in a detention cell, through facial recognition automatically is a big, exciting problem and there are many different approaches to solve this problem because it must detect multiple faces (multi-face). Especially in uncontrolled real-life scenarios, faces will be seen from various sides and not always facing forward, which makes classification problems more difficult to solve. In this research, one method is combined deep neural networks that is Convolutional Neural Networks (CNN) and Haar Cascade Classifier as real-time facial recognition, which has proven to be very efficient in face classification. Methods are implemented with assistance library Open-CV for multi-face detection and 5MP CCTV camera devices. In preparing the architectural model Convolutional Neural Networks Do configuration parameter initialization to speed up the network training process. Test results on 51 test data using constructs Convolutional Neural Networks VGG16 models up to a depth of 16 layers of convolution layers with input from the extraction of the Haar Cascade Classifier resulting in facial recognition system performance reaching an accuracy rate of about 87%.