Face Detection Algorithm Research Articles

Facial recognition using Haar cascade and LBP classifiers

Facial Recognition is the biometric technique used in face detection. The task for validating or recognizing a face from the multi-media photographs is done using facial recognition technique. With the evolution of advanced society the requirement for face identification has been really important. Detection and identification of faces has been grown worldwide. It owes the demand for security such as authorization, national safety and other vital circumstances. There are number of algorithms for facial detection. This paper aspires to present the comparison of two face recognition techniques Haar Cascade and Local Binary Pattern edified for the classification. As a result the accuracy of Haar Cascade is more than the Local Binary Pattern but the execution time in Haar Cascade is more than Local Binary Pattern.

A SYSTEM DESIGN OVERVIEW ON FACE RECOGNITION SECURITY SYSTEM

Nowadays, security is the essential part of systematic day to day activities. By utilizing internet of things, there is a growing attentiveness in the smart home technique. The security capacity is one of the main aspects in the smart home technique which can just lock and unlock the entryway. At present, most of the security problems are being faced at the public places such as banks, offices, shopping complexes etc. Hence, to obtain the desired security many exclusively expert workers are required. The human beings as a worker make mistakes that might influence the level of security. The face recognition security system is a recommended solution to the above-mentioned problem, which can find intruders to confined areas and human mistakes are decreasing by using this system. This system includes two types of components namely hardware component and software components. The hardware components include webcam, Arduino microcontroller, PIR sensor and LCD display. The software components include face detection and face recognition algorithm. The face recognition system is presented to take images very quickly. The webcam captures the images after any movement is detected by PIR sensor in a particular area. Subsequently, in the captured image the face is detected and recognized. Ultimately, by using IOT applications images and notifications are sent to the smart phone of an authorized person.

Attendance Recording System using Face Recognition

Image processing automated attendance system is the system in which easiest way to record the attendance for organization .This system is based on the face detection and face recognition algorithms. For this we make use of “Image Processing” using “MATLAB”. The concept of this paper is to provide real time attendance of students in a class to the faculty’s data base. Automatically detects the student using the web camera and only detect the facial part of that particular image and the image undergoes the various techniques and will compare with reference image, Later the attendance of the student is updated .Thus with the help of this system time will be saved and it is so convenient to record the attendance at any time throughout the day.

A Deep Convolutional Neural Network to Limit Virus Spread Using Facial Mask Segmentation

Due to the recent COVID-19 outbreak the world has experienced many challenges. Limit and control the virus spread rate is one of them. This letter focuses on limiting the speed of virus spreading by monitoring the use of facial mask in crowded public environments such as tourism places, commercial centres, etc. The proposed method first accurately localizes faces using a state-of-the-art approach and, segments facial mask in a second step. The facial mask segmentation allows to distinguish whether the current subject is wearing a facial mask or not but also if it is properly covering the human face. Indeed, most recent face detection algorithms provide as output a set of facial features such as nose tip and mouth corners. By combining these facial features with facial mask segmentation, the proposed method detects real-time subjects that indirectly encourage virus spread in crowded environments. The proposed facial mask segmentation model is trained with pairs of RGB images and its corresponding alpha image created by extending the publicly available real-world masked face dataset. Further, the proposed model is pruned and optimized using the TensorRt library to be usable for real-world applications.

A Fast and Simple Face Detection Algorithm Using Neural Network and Its Implementation on FPGA

Face recognition is one of the interesting types of biometric which determines the presence or absence of human faces in the picture. In this paper, a face recognition system is presented that benefits from an optimized architecture based on the MLP neural network. The proposed method considerably improves the speed and the accuracy of detection compared to traditional architectures of neural network. To reduce the overall computation, neural network is organized so that to be able to rule out the majority of the non-image areas located in the image’s background before applying the main algorithm. An important advantage of this new architecture is its homogeneous structure that makes it suitable for optimized implementation on a hardware platform. In this work, FPGA is used as the platform for implementation of the proposed algorithms. The implementation was done considering Taylor expansion of the activation functions. The performance of the proposed method and the implemented system was evaluated on the BioID dataset. Accomplishment of the proposed method is high precision while reducing training time and total calculations, together with appropriate robustness. Finally, a comparison with other face recognition methods has been done to show the performance of the presented system. The comparison result shows that the proposed system outperforms the other mentioned methods.

Automatic Attendance Recording System Using Facial Recognition

The student attendance is collected either manually or digitally through an RFID card. Recently, some of the institutions adopted the use of dynamic QR codes for attendance monitoring. However, these systems are challenging to maintain and time-consuming. The student can easily manipulate signatures for attendance monitoring. Moreover, the QR code system requires the application on both the lecturer and student sides. The lecturer needs to set up the QR code while using the mobile app to use the system. Therefore, it is time-consuming and sacrifices several minutes at the beginning of each class. The student also sometimes has difficulty in using the app leading to manual data insertion. Therefore, this project aims to solve this issue by using the facial recognition system on a lecturer’s laptop to collect the attendances. This project requires the use of a webcam and MATLAB to perform facial image recognition. This work utilizes face detection algorithm in the MATLAB image processing toolbox to build a system that will detect and recognize the frontal faces of students in a classroom. Firstly, several images of students will be recorded in a database. Then, a GUI-based application automatically identifies a face and matches it with the database created. Lecturers will use the GUI on their laptops, and students will show their faces to the web camera for their attendance to be taken. The system was tested successfully where student face was successfully recognized and recorded in the attendance monitoring system. Only registered user faces will be detected.

Computational appraisal of gender representativeness in popular movies

Gender representation in mass media has long been mainly studied by qualitatively analyzing content. This article illustrates how automated computational methods may be used in this context to scale up such empirical observations and increase their resolution and significance. We specifically apply a face and gender detection algorithm on a broad set of popular movies spanning more than three decades to carry out a large-scale appraisal of the on-screen presence of women and men. Beyond the confirmation of a strong under-representation of women, we exhibit a clear temporal trend towards fairer representativeness. We further contrast our findings with respect to a movie genre, budget, and various audience-related features such as movie gross and user ratings. We lastly propose a fine description of significant asymmetries in the mise-en-scène and mise-en-cadre of characters in relation to their gender and the spatial composition of a given frame.

WITHDRAWN: Survey of intelligent surveillance system for monitoring international border security

Abstract One of the most difficult tasks in all countries is to monitor the security of international borders. Border security forces have no ability for monitoring long borders all the time and in all seasons. Robots can be used to detect intruders at the borders and then transmit the information to the control center. In hostile environment, various high-risk tasks are performed excellently via robots, whereas it might cause danger to soldiers. In this work, the major aim is to suggested a design for surveillance system in terms of monitoring the security of international borders. The suggested design consists of an automated vehicle with a monitoring system on the basis of face recognition and detection algorithms, the human’s presence is checked via the system which after that runs the algorithm for face recognition. In the case when the identified person’s face data doesn’t match the already-stored soldiers’ personal data, then the person will be recognized by the system as intruder and the system will send a message to the central control. Through the comparison tables for previous work, we suggest using HAAR algorithm for face detection and CNN algorithm for recognition by the use of raspberry pi and open-cv library. By studying a variety of monitoring technologies which involves the development of many robots with the use of raspberry-pi technology, the paper reports that all approaches are attempting to provide better results regarding the quality of video transmission and attempt to enhance the effectiveness with regard to the time spent on video transmission and face detection algorithm.

Personalized Emotion Recognition and Emotion Prediction System Based on Cloud Computing

Promoting economic development and improving people’s quality of life have a lot to do with the continuous improvement of cloud computing technology and the rapid expansion of applications. Emotions play an important role in all aspects of human life. It is difficult to avoid the influence of inner emotions in people’s behavior and deduction. This article mainly studies the personalized emotion recognition and emotion prediction system based on cloud computing. This paper proposes a method of intelligently identifying users’ emotional states through the use of cloud computing. First, an emotional induction experiment is designed to induce the testers’ positive, neutral, and negative three basic emotional states and collect cloud data and EEG under different emotional states. Then, the cloud data is processed and analyzed to extract emotional features. After that, this paper constructs a facial emotion prediction system based on cloud computing data model, which consists of face detection and facial emotion recognition. The system uses the SVM algorithm for face detection, uses the temporal feature algorithm for facial emotion analysis, and finally uses the classification method of machine learning to classify emotions, so as to realize the purpose of identifying the user’s emotional state through cloud computing technology. Experimental data shows that the EEG signal emotion recognition method based on time domain features performs best has better generalization ability and is improved by 6.3% on the basis of traditional methods. The experimental results show that the personalized emotion recognition method based on cloud computing is more effective than traditional methods.

Intelligent Drive Assistance System

The report proposes the research conducted and the project made in the field of computer engineering to develop a system for driver drowsiness detection to prevent accidents from happening because of driver fatigue and sleepiness. The report proposed the results and solutions on the limited implementation of the various techniques that are introduced in the project. Whereas the implementation of the project give the real world idea of how the system works and what changes can be done in order to improve the utility of the overall system. Furthermore, the paper states the overview of the observations made by the authors in order to help further optimization in the mentioned field to achieve the utility at a better efficiency for a safer road. A person driving needs to be able to focus on driving at all instances. Any prolonged or sudden complications to the person driving the vehicle can cause serious accidents/damages. To ignore the importance of this could result in severe physical injuries, deaths and economic losses. Road incidents remain the leading type of fatal work-related event, carrying tremendous personal, social, and economic costs. While employers with a fixed worksite can observe and interact directly with workers in an effort to promote safety and reduce risk, employers with workers who operate a motor vehicle as part of their job have fewer options. Drowsiness detection system is regarded as an effective tool to reduce the number of road accidents. This project proposes a non-intrusive approach for detecting drowsiness in drivers, using Computer Vision. Developing various technologies for monitoring and preventing drowsiness while driving is a major trend and challenge in the domain of accident avoidance systems. This project proposes a non-intrusive approach for detecting drowsiness in drivers, using Computer Vision. Developing various technologies for monitoring and preventing drowsiness while driving is a major trend and challenge in the domain of accident avoidance systems. Haar face detection algorithm is used to capture frames of image as input and then the detected face as output.

Personal identification system based on multi biometric depending on cuckoo search algorithm

In modern devices, many personal identification systems are used using various biometrics to confirm the identity of an individual and identify him for several purposes. Some of these essential biometrics are used in this paper to help identify a person while attaining social distance because of the widespread epidemics. The features of the face, eyes, nose, and finally, the features of the mouth are used in this article. The work begins by detecting the parts of multibiometric from the input images using Viola-Jones face detection algorithm with a modification to it then segment them. After that, various initial treatment processes begin which helps clarify them to facilitate subsequent operations. Also, a Histogram of oriented gradient method(HOG) is used to extract the significant features of those image segments. The extracted features from these segments are entered into the developed cuckoo search algorithm(DCSA), the best image segment within the used dataset is searched for similar in terms of characteristics to the entered image segment. The work has also been developed so that the system is executed on two cores using parallel processing technology to utilize the processor as much as possible and reduce the time it takes to implement the system and identify the person concerned. A high identification rate has been reached, reaching 99.25%, and overall speed up 1.40323 sec relative to serial execution.

License Verification System With Face Recognition Using IOT

A Driving license identification system as a part of smart city development. Driving license system is a huge task for the government to monitor. Whenever the person gets the license that time, the face of the person is stored in the database. Haar-Cascade Classifier algorithm is used for face detection and Local Binary Pattern algorithm for recognition technology. The hardware components are cost effective, small in size and has sufficient computational power for application-oriented components. The frivolous nature of licence owners makes it hard to take care of the documents they hold and brings the job of police officers to critical state when verifying the details. This project prevails in the way for replacing the usage of hard copies with digital footprint. To overcome this problem face detection and finger print based license authentication system using IOT will be implemented. Raspberry pi is the brain of this system, which helps for face detection and face recognition. The USB camera gets interfaced with raspberry pi to get the data from user.All these data are uploaded to the cloud (IOT) through NodeMCU . It is used to find the person having license or not and also get the validation of the license. Whenever the person doesn’t have a legitimate license or if the license is already expired, the display indicates it is invalid and vice versa.

A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets

Social media play an important role in the daily life of people around the globe and users have emerged as an active part of news distribution as well as production. The threatening pandemic of COVID-19 has been the lead subject in online discussions and posts, resulting to large amounts of related social media data, which can be utilised to reinforce the crisis management in several ways. Towards this direction, we propose a novel framework to collect, analyse, and visualise Twitter posts, which has been tailored to specifically monitor the virus spread in severely affected Italy. We present and evaluate a deep learning localisation technique that geotags posts based on the locations mentioned in their text, a face detection algorithm to estimate the number of people appearing in posted images, and a community detection approach to identify communities of Twitter users. Moreover, we propose further analysis of the collected posts to predict their reliability and to detect trending topics and events. Finally, we demonstrate an online platform that comprises an interactive map to display and filter analysed posts, utilising the outcome of the localisation technique, and a visual analytics dashboard that visualises the results of the topic, community, and event detection methodologies.

Automated Attendance System and Voice Assistance using Face Recognition

In this work we propose a Live Attendance Marking System for institutional purpose. This system will enable the department to mark the attendance of students automatically by recognizing their faces. The system is based on the face detection and recognition algorithms and automatically recognizes a student whenever he/she comes across the camera module. Further after the recognition, it automatically updates his/her attendance in the Cloud web server database. The basic architecture and all the algorithms used in the model are described in the paper elaborately. Moreover in order to evaluate and enhance the performance of the system many of the filter functions are used which provide the capability to capture and recognize the images even in dull and low light places. This project also suggests the technique to ensure that a student can mark the attendance only once in a day. The live attendance system is much more efficient to traditional attendance systems both in saving time and in maintaining the database.

Vision-Based Road Rage Detection Framework in Automotive Safety Applications.

Drivers’ road rage is among the main causes of road accidents. Each year, it contributes to more deaths and injuries globally. In this context, it is important to implement systems that can supervise drivers by monitoring their level of concentration during the entire driving process. In this paper, a module for Advanced Driver Assistance System is used to minimise the accidents caused by road rage, alerting the driver when a predetermined level of rage is reached, thus increasing the transportation safety. To create a system that is independent of both the orientation of the driver’s face and the lighting conditions of the cabin, the proposed algorithmic pipeline integrates face detection and facial expression classification algorithms capable of handling such non-ideal situations. Moreover, road rage of the driver is estimated through a decision-making strategy based on the temporal consistency of facial expressions classified as “anger” and “disgust”. Several experiments were executed to assess the performance on both a real context and three standard benchmark datasets, two of which containing non-frontal-view facial expression and one which includes facial expression recorded from participants during driving. Results obtained show that the proposed module is competent for road rage estimation through facial expression recognition on the condition of multi-pose and changing in lighting conditions, with the recognition rates that achieve state-of-art results on the selected datasets.

A Framework of an Intelligent Education System for Higher Education Based on Deep Learning

Intelligent learning platforms and education information application platforms are gaining ground, owing to the wide application of modern technologies such as the Internet of Things, big data analysis, artificial intelligence, and cloud computing. However, the current platforms cannot solve specific teaching problems, and the relevant research mostly focuses on primary and secondary education. Therefore, this paper constructs and analyzes a framework of intelligent education system for higher education based on the deep learning. Firstly, the functional block diagram of the system was built up. Next, a face detection algorithm was proposed based on the multi-task convolutional neural network, a face recognition algorithm was developed based on the improved deep convolutional neural network, and the knowledge learning status of students was tracked based on the memory augmented neural network. Finally, the proposed framework was proved effective and swift through experiments. The research results expand the application scope of the deep learning in education.

Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People

This work is part of an ongoing research project to develop an unmanned flying social robot to monitor dependants at home in order to detect the person’s state and bring the necessary assistance. In this sense, this paper focuses on the description of a virtual reality (VR) simulation platform for the monitoring process of an avatar in a virtual home by a rotatory-wing autonomous unmanned aerial vehicle (UAV). This platform is based on a distributed architecture composed of three modules communicated through the message queue telemetry transport (MQTT) protocol: the UAV Simulator implemented in MATLAB/Simulink, the VR Visualiser developed in Unity, and the new emotion recognition (ER) system developed in Python. Using a face detection algorithm and a convolutional neural network (CNN), the ER System is able to detect the person’s face in the image captured by the UAV’s on-board camera and classify the emotion among seven possible ones (surprise; fear; happiness; sadness; disgust; anger; or neutral expression). The experimental results demonstrate the correct integration of this new computer vision module within the VR platform, as well as the good performance of the designed CNN, with around 85% in the F1-score, a mean of the precision and recall of the model. The developed emotion detection system can be used in the future implementation of the assistance UAV that monitors dependent people in a real environment, since the methodology used is valid for images of real people.

Face Authentication System Embedded with PIC18F458 for Automated Teller Machines

Face recognition system is one of the biometric information process. Its applicability is easier and working range is wider than other biometric systems like signature, fingerprint, iris, etc. This paper proposes, a face recognition based authentication scheme for automated teller machine banking systems. The designed detection method extract face features from input image. The output image of the face detection algorithm has to be similar with the input image recognized for successful authentication. The proposed PIC18F458 Microcontroller based face recognition biometric scheme is successfully fused with the automated teller machine banking system for personal authorization with increased social security.

Face Authentication System Embedded with PIC18F458 for Automated Teller Machines

Face recognition system is one of the biometric information process. Its applicability is easier and working range is wider than other biometric systems like signature, fingerprint, iris, etc. This paper proposes, a face recognition based authentication scheme for automated teller machine banking systems. The designed detection method extract face features from input image. The output image of the face detection algorithm has to be similar with the input image recognized for successful authentication. The proposed PIC18F458 Microcontroller based face recognition biometric scheme is successfully fused with the automated teller machine banking system for personal authorization with increased social security.

RETRACTED ARTICLE: Research on intelligent classroom attendance management based on feature recognition

The regular attendance method is affected by many factors in the classroom intelligent attendance, which leads to lower attendance efficiency. In order to improve the efficiency of intelligent classroom attendance management, this study uses face recognition technology as a technology to study the attendance mode of face feature recognition. In the study, we used the multi-task cascade convolutional neural network to identify the face features and used the MTCNN face detection algorithm frame by frame for the sampled frame images and normalized the single face region image cropping to the face recognition model input size. Thereafter, the normalized single face region image is taken for feature extraction. In addition, this paper analyzes the effectiveness of the algorithm by design comparison experiments. The research shows that the algorithm proposed in this study has certain practical effects and can provide theoretical reference for subsequent related research.