scholarly journals Design of Intelligent Facial Recognition System using AI for Surveillance Application

2020 ◽  
pp. 346-356
Author(s):  
Syed Ibrahim ◽  
Syed Nahid Suleman ◽  
Manikanta Suthapalli ◽  
Abhishek Sharma ◽  
Shilpa K S
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Facial Recognition ◽  
Recognition System ◽  
Security System ◽  
Software Module ◽  
High Security ◽  
Trained Staff ◽  
Facial Recognition System ◽  
Surveillance Application

Organizations presently continue to encounter significant security concerns; consequently, they require much particularly trained staff to achieve the coveted protection. This staff performs blunders that may affect the extent of security. A suggested solution to the matter mentioned above is a Face Recognition Security System, which can monitor and identify trespassers to blocked or high-security areas and assist in overcoming the margin of manual human oversight. This system is comprised of two halves: the hardware part and the software part. The hardware module incorporates a camera, while the software module includes software that uses face-detection and face-recognition algorithms. If a person infiltrates the confine in question, a set of snaps are captured by the camera and dispatched to the software to be examined/identified and equated with an existent database of trusted people. An alert is conveyed to the user if the infiltrator is not recognized.

scholarly journals Design of Facial Recognition System Based on Visual Communication Effect

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xuhui Fu
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Visual Communication ◽  
Facial Recognition ◽  
Recognition System ◽  
Image Capture ◽  
Hardware System ◽  
Face Recognition System ◽  
The Face ◽  
Facial Recognition System

At present, facial recognition technology is a very cutting-edge science and technology, and it has now become a very hot research branch. In this research, first, the thesis first summarized the research status of facial recognition technology and related technologies based on visual communication and then used the OpenCV open source vision library based on the design of the system architecture and the installed system hardware conditions. The face detection program and the image matching program are realized, and the complete face recognition system based on OpenCV is realized. The experimental results show that the hardware system built by the software can realize the image capture and online recognition. The applied objects are testers. In general, the OpenCV-based face recognition system for testers can reliably, stably, and quickly realize face detection and recognition in this situation. Facial recognition works well.

scholarly journals Design and Implementation of Facial Recognition System for Visually Impaired using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 4803-4807
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Visually Impaired ◽  
Recognition System ◽  
Raspberry Pi ◽  
Design And Implementation ◽  
The Face ◽  
Visually Impaired Students ◽  
Facial Recognition System

One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are preprocessed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.

scholarly journals Facial recognition system using LBPH face recognizer for anti-theft and surveillance application based on drone technology

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1070-1077
Author(s):  
Li Wang ◽  
Ali Akbar Siddique
Keyword(s):  
Global Positioning System ◽  
Facial Recognition ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Positioning System ◽  
Complex Task ◽  
Global Positioning ◽  
The World ◽  
Facial Recognition System ◽  
Surveillance Application

Providing security to the citizens is one of the most important and complex task for the governments around the world which they have to deal with. Street crimes and theft are the biggest threats for the citizens and their belonging. In order to provide security, there is an urgent need of a system that is capable of identifying the criminal in the crowded area. This paper proposes a facial recognition system using Local Binary Patterns Histogram Face recognizer mounted on drone technology. The facial recognition capability is a key feature for a drone to have in order to find or identify the person within the crowd. With the inception of drone technology in the proposed system, we can use it as a surveillance drone as well through which it can cover more area as compared to the stationary system. As soon as the system identifies the desired person, it tags him and transmits the image along with the co-ordinates of the location to the concerned authorities using mounted global positioning system. Proposed system is capable of identifying the person with the accuracy of approximately 89.1%.

scholarly journals Enhancement of Face Recognition using Deep Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 5072-5075
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Feature Detection ◽  
Facial Recognition ◽  
Recognition System ◽  
Detection Capability ◽  
Comparative Result ◽  
Facial Recognition System

Our aim in this paper is to increase the accuracy of existing facial recognition system on a comparative smaller dataset as per the requirements of present day. Namely in sensitive regions. The methodology that has been adopted is by combining more than one algorithms. The feature detection capability of harr cascade along with Ada boost to fetch to Bilinear CNN so that on a comparative smaller dataset can produce comparative result as on bigger dataset.

scholarly journals Efficient Face Recognition System for Operating in Unconstrained Environments

2021 ◽  
Vol 7 (9) ◽  
pp. 161
Author(s):  
Alejandra Sarahi Sanchez-Moreno ◽  
Jesus Olivares-Mercado ◽  
Aldo Hernandez-Suarez ◽  
Karina Toscano-Medina ◽  
Gabriel Sanchez-Perez ◽  
...  
Keyword(s):  
Real Time ◽  
Face Detection ◽  
High Speed ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Processing System ◽  
Recognition System ◽  
Support Vector ◽  
The Face ◽  
Facial Recognition System

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.

scholarly journals SOME PROBLEMS OF USING THE FACIAL RECOGNITION SYSTEM IN LAW ENFORCEMENT ACTIVITIES

2021 ◽  
Vol 17 (1(63)) ◽  
pp. 189-200
Author(s):  
Василий Васильевич ЯРОВЕНКО ◽  
Галина Михайловна ШАПОВАЛОВА ◽  
Ринат Альбертович ИСМАГИЛОВ
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
Information Technologies ◽  
Facial Recognition ◽  
Recognition System ◽  
State Regulation ◽  
Private Life ◽  
Dialectical Logic ◽  
Advantages And Disadvantages ◽  
Facial Recognition System

The article draws attention to the problems of the use of modern software and hardware tools and methods of facial fixing and recognizing by law enforcement agencies. In using various techniques aimed at obtaining information on a person’s physiological and biological characteristics, it is important to respect not only his or her right to protect the data, but also state interests in combating crime (terrorism, corruption). Important factors are state regulation and the development of norms for the effective use of information technologies, telecommunications and artificial intelligence technologies so that citizens do not doubt their effectiveness and legitimacy. Purpose: to analyze current problems of combating crime; to submit proposals for improving the application of the facial recognition system, and the establishment by the Ministry of Internal Affairs of a single biometric database of Russians with the strictest compliance with citizens' constitutional rights to privacy, reliable protection of their personal data. Methods: the authors use empirical methods of comparison, description, interpretation as well as theoretical methods of formal and dialectical logic. Results: the study reveals the problems of using the facial recognition system, the advantages and disadvantages of the system are analyzed. On the one hand, in the Russian Federation there are no clear instructions and an algorithm for the use of face-recognition cameras, which would satisfy society’s requirements to protect private life and personal and family privacy. On the other hand, face-recognition cameras can assist law enforcement authorities in locating wanted persons and detecting (solving) crimes.

scholarly journals Face recognition for automatic vehicle ignition based on Raspberry Pi

2020 ◽  
Vol 1 (2) ◽  
pp. 53-68
Author(s):  
Alex V. Nuñez ◽  
Liliana N. Nuñez
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Facial Recognition ◽  
Data Gathering ◽  
Software Component ◽  
Security System ◽  
Raspberry Pi ◽  
Hardware Platform ◽  
And Training

In this project a facial recognition application for automatic vehicle ignition is developed. This application is built using a Raspberry Pi as the hardware platform and the OpenCV library for computer vision as the software component. In this research the different methods for automobile security are analyzed, as well as, the different methods used to perform face recognition.  The main goal of this application is to enhance the security system of the vehicle, allowing to ignite the vehicle only by register users. To achieve this goal three main processes are carried out, face detection, data gathering, and training the system to grant access through face recognition.

scholarly journals An Separation of Different Facial Expression Recognition System

2019 ◽  
Vol 8 (6S4) ◽  
pp. 438-441
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Digital Data ◽  
Expression Recognition ◽  
Share Data ◽  
The Face ◽  
Facial Recognition System

Face recognition is one of the hot topics in the current world and one of the popular topics of computer studies. Today face recognition in the network society and access to digital data is gaining more attention. The facial recognition system technology is a biometric assessment of a human's face. There are many facial recognition techniques that are intended depending on facial expressions extraction, one of which is 3D facial recognition, as well as their fusion,is difficult. During preprocessing measures for picture recognition to remove only expression-specific characteristics from the face and prevent their issues with a convolution neural network. We can also use some theorems such as LBP and Taylor's theorem to model face recognition. In particular, for cloud robots, we can also use this facial recognition on robots. The robot can perform functions and share data between servers and devices. Seven fundamental expressions are used to identify and classify: happiness, shock, fear, disgust, sadness, rage, and a neutral condition. Until now, the recognition rate is quite up to the expectation stage, but it still tries to enhance. To enhance the recognition frequency of facial image recognition, feelings are chosen by the vibrant Bayesian network technique to depict the development of facial awareness in addition to various emotional operations of facial expressions. The ICCA techniques involve various multivariate sets of distinct facial features that could be eyes, nose, and mouth.

scholarly journals INTEGRATION OF USER INTERFACE WITH FACIAL RECOGNITION SYSTEM

2020 ◽  
pp. 111-115
Author(s):  
D.Manasa ◽  
N.Ramya Sri ◽  
Sk.Naveed ◽  
N.Ramya
Keyword(s):  
Face Recognition ◽  
High Probability ◽  
Facial Recognition ◽  
Recognition System ◽  
Probability Of Error ◽  
Traditional System ◽  
The Face ◽  
Facial Recognition System ◽  
Python Programming ◽  
Recognition Technique

Attendance of students in a large classroom is hard to be handled by the traditional system, as it is time-consuming and has a high probability of error during the process of inputting data into the computer. This paper proposed automated attendance marking system using face recognition technique. The system will help to find the positive and negative of the face and Eigen face algorithm for face recognition by using python programming and OpenCV library. The proposed method using PCA to resolve the problems such as lightning of the images, and the direction of the student faces. The attendance of the student was updated to the Excel sheet after student's face has been recognized. KEYWORDS: PCA, Facial Recognition, ERP, Classroom, Attendance

scholarly journals Spartan Face Mask Detection and Facial Recognition System

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 87
Author(s):  
Ziwei Song ◽  
Kristie Nguyen ◽  
Tien Nguyen ◽  
Catherine Cho ◽  
Jerry Gao
Keyword(s):  
Deep Learning ◽  
Face Detection ◽  
Facial Recognition ◽  
Learning Algorithms ◽  
Face Mask ◽  
Recognition System ◽  
World Health ◽  
Identity Recognition ◽  
Facial Recognition System ◽  
Health Organization

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.

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