scholarly journals GSM based Face Recognition using Pir Sensor on Raspberry Pi 3

2019 ◽  
Vol 8 (12) ◽  
pp. 329-332
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Area ◽  
Detection And Identification ◽  
Face Recognition System ◽  
Characteristic Views ◽  
The Face ◽  
Area Unit ◽  
Human Faces

We Developed An Associate Approach To The Detection And Identification Of Human Faces And Describe A Operating, Near-Real-Time Face Recognition System That Tracks A Subject’s Face And So Acknowledges The Person By Comparison Characteristics Of The Face To Database. Our Approach Treats Face Recognition As A Two-Dimensional Recognition Downside, Taking Advantage Of The Very Fact That Faces Area Unit Area Unit Normally Upright And Therefore Is Also Delineate By A Small Set Of 2-D Characteristic Views. Face Pictures Are Projected Onto A Feature Area (“Face Space”) That Best Encodes The Variation Among Database Images. The Face Area Is Outlined By The “Eigenfaces”, That Area Unit The Eigenvectors Of The Set Of Faces; They Do Not Essentially Correspond To Isolated Options Like Eyes, Ears, And Noses. The Framework Provides The Flexibility To Be Told To Acknowledge New Faces

scholarly journals Smart Door Lock/Unlock Using Raspberry Pi

2020 ◽  
pp. 543-548
Author(s):  
Sandesh R ◽  
Avinash Sridhar ◽  
Rishikesh T P ◽  
Saniya Farheen ◽  
Sara Tameem
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Images ◽  
Face Features ◽  
Image Capturing ◽  
The Face ◽  
Face Detection And Recognition ◽  
Door Lock ◽  
Human Faces

This paper deals with the proposed system for smart and savvy door lock recognition system which is essentially for identification of human faces and mainly for home security. This is divided into two sub systems. First is image capturing, then comes face detection and recognition and finally automatic door access management. Open CV is mainly used for Face Recognition because it uses Eigen faces which compares the face images and produces it without losing vital face features, facial images of various persons are going to be stored in database. The purpose of the paper is to take face recognition to height which can replace the use of standard passwords, pins and patterns, adding more security to our life. The process carried out by raspberry pi is fast and makes the system work smoother.

Smart Door for Handicapped People via Face Recognition and Voice Command Technique

2021 ◽  
Vol 39 (1B) ◽  
pp. 222-230
Author(s):  
Hana'a M. Salman ◽  
Rana T. Rasheed
Keyword(s):  
Face Recognition ◽  
Smart Home ◽  
High Performance ◽  
Cost Effective ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
The Face ◽  
Handicapped People ◽  
Facial Identification

Smart home indicates an application for different technological implementations, it could indicate any system which controls the door lock and several other devices. Facial identification which is an important section to achieve surveillance and safety, especially for handicapped people, can be considered as one of the ways that deal with biometrics and performed to identify facial images via utilizing fundamental features of the face. A Raspberry Pi-based face recognition system using conventional face detection and recognition techniques is going to be supplied, so the method in which image-built biometrics uses a Raspberry Pi is described. The aim of the paper here can be considered as transferring face recognition to a level in which the system can replace the utilizing of RF I-Cards and a password to access any system of security and making the system alive and protect the door from being open by hackers, especially by using the picture of an authorized person, we make the raspberry pi turn off and cannot turn on only by a command from the authorized person's mobile. The result of the presented proposal is a system that uses face recognition by utilizing OpenCV, Raspberry Pi, and it functions on an application of Android, and this system percentage becomes 99.63%. It should be cost-effective, of high performance, secured, and easy to use, which can be used in any smart home application.

scholarly journals A Face Recognition Method in the Internet of Things for Security in Smart Recognition Places

2022 ◽  
Vol 10 (1) ◽  
pp. 687-690
Author(s):  
Prof. Kalpana Malpe
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Associate Degree ◽  
Human Life ◽  
Recognition System ◽  
Raspberry Pi ◽  
Living Things ◽  
Face Recognition System ◽  
The Face ◽  
Face Detection And Recognition

Abstract: In recent years, the safety constitutes the foremost necessary section of the human life. At this point, the price is that the greatest issue. This technique is incredibly helpful for reducing the price of watching the movement from outside. During this paper, a period of time recognition system is planned which will equip for handling pictures terribly quickly. The most objective of this paper is to safeguard home, workplace by recognizing individuals. The face is that the foremost distinctivea part of human’s body. So, it will replicate several emotions of associate degree Expression. A few years past, humans were mistreatment the non-living things like good cards, plastic cards, PINS, tokens and keys for authentication, and to urge grant access in restricted areas like ISRO, National Aeronautics and Space Administration and DRDO. The most necessary options of the face image are Eyes, Nose and mouth. Face detection and recognition system is simpler, cheaper, a lot of accurate, process. The system under two categories one is face detection and face recognition. Throughout this case, among the paper, the Raspberry Pi single-board computer is also a heart of the embedded face recognition system. Keywords: Raspberry Pi, Face recognition system

scholarly journals Face Recognition Door Unlock System

2019 ◽  
Vol 8 (11S) ◽  
pp. 1133-1139
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Facial Recognition ◽  
First Grade ◽  
Recognition System ◽  
Video Frame ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Essential Components ◽  
The Face

Face recognition system is widely used for human identification particularly for security functions. The project deals with the look and implementation of secure automatic door unlockby using Raspberry Pi. Web camera for capturing the images from the video frame is operated and controlled by raspberry pi using Open CVPython library to train and store human faces for recognition. In this project we are using Raspberry Pi as face recognition module to capture human images and it will compare with stored data base images. If it matches with authorized user then system allows to supply power to electromagnetic lock to create magnetic field for unlocking the door. The need for facial recognition system that is fast and accurate is continuously increasing which can detect intruders and restricts all unauthorized users from highly secured areas and aids in minimizing human error. Face recognition is one of the most Secured System than the biometric pattern recognition technique which is used in a large spectrum of applications.The time and accuracy factor is considered about the major problem which specifies the performance of automatic face recognition in real time environments. Various solutions have been proposed using multicore systems. By considering present challenge, this provides the complete architectural design and proposes an analysis for a real time face recognition. Thus, the image extracted and allowed to match with the database pictures. If the images are matched, the door unlocks mechanically. the planning of the face recognition system exploitation Raspberry pi will create the smaller, lighter and with lower power consumption, therefore it's a lot of convenient than the PC-based face recognition system. Principle element analysis LBPH (Local Binary Pattern Histogram) algorithmic program is employed for the face recognition and detection method. Then acknowledgement are send through Zigbee module from transmitter to receiver. If image isn't detected in database then it'll ask for manual four digit pin for unlocking the door.The developed theme is affordable, fast, and extremely reliable and provides enough flexibility to suits any environment of various systems. Problem Statement:In theworld of emerging technology, security became an essential component in day to day life. Information theft, lack of security and violation of privacy etc. are the essential components which are needed to be protected. Using smart secure systems for door lock and unlocking became popular nowadays. This is system is being adapted by many countries and first grade countries such as USA, Japan etc., already makes use of this system. This system provides either a facial recognition security feature or a keypad is provided to enter the passcode which unlocks the door. Although, it provides security to the doors, it also has somelimitations and drawbacks: Firstly, if the system mainly uses a facial recognition module, there might be a slight chance that sometimes the face may not be detected and hence the door cannot be unlocked. Secondly, if the system uses a

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

scholarly journals Integarted Minimum Cost Sub-Block Matching Distance based Face Recognition using Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 117-122
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Minimum Cost ◽  
Recognition System ◽  
Block Matching ◽  
Face Region ◽  
Input Face ◽  
Face Recognition System ◽  
The Face ◽  
Fuzzy C Means Clustering

Now a days one of the critical factors that affects the recognition performance of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The face recognition approach that we proposed, detects the face region that is not occluded and then uses this region to obtain the face recognition. To segment the occluded and non-occluded parts, adaptive Fuzzy C-Means Clustering is used and for recognition Minimum Cost Sub-Block Matching Distance(MCSBMD) are used. The input face image is divided in to number of sub blocks and each block is checked if occlusion present or not and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results shows our method is giving promising results when compared to the other conventional techniques.

scholarly journals A FACIAL RECOGNITION USING OPEN COMPUTER VISION

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr.C K Gomathy ◽  
T. suneel ◽  
Y.Jeeevan Kumar Reddy
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Functional Space ◽  
Recognition System ◽  
Statistical Technique ◽  
Exciting Field ◽  
Two Dimensional Image ◽  
Face Recognition System ◽  
The Face ◽  
Amount Of Knowledge

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.

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

scholarly journals Real-Time Internet Based Attendance Using Face Recognition System

2018 ◽  
Vol 7 (3.15) ◽  
pp. 174 ◽  
Author(s):  
Yuslinda Wati Mohamad Yusof ◽  
Muhammad Asyraf Mohd Nasir ◽  
Kama Azura Othman ◽  
Saiful Izwan Suliman ◽  
Shahrani Shahbudin ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Recognition System ◽  
Ease Of Use ◽  
Cloud Services ◽  
Raspberry Pi ◽  
Student Attendance ◽  
Online Student ◽  
Internet Server ◽  
Face Recognition System

This project focuses on face recognition implementation in creating fully automated attendance system with a cloud. Cloud services will provide a useful information regarding the attendance such as attendance summary performance and visualizing the data into graph and chart. In this study, we aim to create an online student attendance database, interfaced with a face recognition system based on raspberry pi 3 model B. A graphical user interface (GUI) will provide ease of use for data analysis on the attendance system. This work used open computer vision library and python for face recognition system combined with SFTP to establish connection to an internet server which runs on PHP and Node.js. The results showed that by interfacing a face recognition system with a server, a real-time attendance system can be built and be monitored remotely.  

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