scholarly journals A smart login system using face detection and recognition by ORB algorithm

2020 ◽  
Vol 20 (2) ◽  
pp. 1078
Author(s):  
Mohammad Jahangir Alam ◽  
Tanjia Chowdhury ◽  
Md. Shahzahan Ali
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Punch Card ◽  
Effective Technique ◽  
Face Images ◽  
Different Types ◽  
Web Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Human Faces

<p>We can identify human faces using a web Camera which is known as Face Detection.  This is a very effective technique in computer technology. There are used different types of attendance systems such as log in with the password, punch card, fingerprint, etc. In this research, we have introduced a facial recognition type of biometric system that can identify a specific face by analyzing and comparing patterns of a digital image.  This system is the latest login system based on face detection. Primarily, the device captures the face images and stores the captured images into the specific path of the computer relating the information into a database. When any body tries to enter into any room or premises through this login system, the system captures the image of that particular person and matches the image with the stored image. If this image matches with the stored image then the system allows the person to enter the room or premises, otherwise the system denies entry. This face recognition login system is very effective, reliable and secured. This research has used the Viola and Jones algorithm for face detection and ORB for image matching in face recognition and Java, MySql, OpenCV, and iReport are used for implementation.</p>

scholarly journals An Efficient Method for Multiple Face Detection & Recognition

2019 ◽  
Vol 9 (1S3) ◽  
pp. 474-478
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Efficient Method ◽  
Recognition Rate ◽  
The Future ◽  
Web Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Future Work ◽  
Detection And Recognition

The problem of Face detection and recognition is becoming a challenge due to the wide variety of faces and the complexity of the noises and background of image. In this paper we have used C-sharp and Haar algorithm to detect the face. First in this paper the image is taken with a web-camera, storing it in the database and then once again when the person comes in the frame the name of the person is displayed. This paper is done in C-sharp which was a bit difficult for us to do and we have combined both the face detection and the recognition. The proposed method has good output and a good recognition rate. The limitation of the paper is that it does not display the name of the person above the face. In the future work will be carried on the above said topic. While developing the code some sample codes in python but those were basic programs. So this paper aims to find a solution for it and developed in C-sharp. In finding the XML file of haarcascade frontal face detection we found some problems and had to do a bit of research in finding it. The code for face detection and face recognition were found in different places and in this paper the codes for the both has been combined and found some difficulty. To overcome the basic programs we have written the code in C-sharp and the difficulty which we faced in combining the two codes have been solved. The solution has been successfully implemented and the code is fully running and the output has been successfully achieved.

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.

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

scholarly journals Human Face Recognition using LBPH

2020 ◽  
Vol 8 (6) ◽  
pp. 3208-3212
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Human Face Recognition ◽  
Smart Systems ◽  
Finger Print ◽  
The Face ◽  
Face Detection And Recognition ◽  
Frame Work

During the beginning of seventieth centuries, human facial recognition has become one among the researched areas in the area of finger print scanning and computer vision. Identifying a person with an image has been popularized through the mass media. The recent technologies are totally focusing on developing the smart systems that will recognize the faces for biometric purposes. In this context automatic face recognition is applied for security purposes to find the criminal, attendance system, scientific laboratories etc. This research paper presents the frame work for real time face detection. However, it is less robust to finger print or retina scanning. This paper describes about the face detection and recognition. These technologies are available in the Open-Computer-Vision (OpenCV) library and methodology to implement them using Python in image processing and machine learning. For face detection, Haar-Cascades algorithms were used and for face recognition the algorithm like Eigen faces, and Local binary pattern histograms were used.

scholarly journals Face with Mask Detection and Recognition for Smart Attendance System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1586-1591
Author(s):  
Shweta Panjabrao Dhawale
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Face Mask ◽  
Learning Framework ◽  
The Face ◽  
Face Detection And Recognition ◽  
Python Programming ◽  
Detection And Recognition

In this paper we will see the face mask detection and recognition for smart attendance system. In current pandemic situation our proposed system is very useful. We have used here face algorithm technique, python programming and to capture the images open cv is used., open cv2 now comes with a very new face recognizer class for the face recognition and popular computer vision liberaay started by intel in 1999. Open cv released under BSD licence that’s why used in the academic projects. We have used the concept of deep learning framework for the detection of faces. our aim is to present the study of previous attempts at face detection and recognition for smart attendance system by using deep learning .these is rapidly growing technology with its application in various aspects.

Attendance Recording System using Face Recognition

2021 ◽  
Vol 9 (VII) ◽  
pp. 432-434
Author(s):  
Chandan R
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Data Base ◽  
Face Detection ◽  
Recording System ◽  
Reference Image ◽  
Web Camera ◽  
The Face ◽  
System Time ◽  
The Web

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.

scholarly journals Identifikasi Wajah Dengan Segmentasi Warna Kulit Menggunakan Metode Viola Jones

2019 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
Andi Asni b ◽  
Tamara Octa Dana
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Skin Color ◽  
Detection System ◽  
Color Segmentation ◽  
Face Images ◽  
Detection Of Objects ◽  
The Face ◽  
Left And Right ◽  
Normal Light

Abstract - Face detection (face detection) is one of the initial steps that is very important before the face recognition process (face recognition). Face detection is the detection of objects in the form of faces in which there are special features that represent the shape of faces in general. One method of face detection is the Viola Jones method. Viola Jones method is used to detect faces and skin color segmentation, test data processing using Matlab and capture on a Smartphone. The test is carried out at normal light intensity with a predetermined distance and face position. The results of this study indicate the level of accuracy of detection of face image variations in the position of face images facing forward (frontal), sideways left and right 45̊. But it has a weakness of this face detection system that is unable to determine faces in images that have faces that are not upright (tilted) or not frontal (facing sideways) at a 90̊ angle. Face position that is upright / not upright will determine the success of this face detection. The level of identification of the Viola Jones simulation was 100% with 4 images consisting of 3 boys and 1 girl.

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 409-416
Author(s):  
Apurva Yawalikar ◽  
Prof. U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

scholarly journals SINGLE-SHOT FACE DETECTION AND RECOGNITION BASED ON CNN

2021 ◽  
pp. 11-20
Author(s):  
Yu. V. Vizilter ◽  
V. S. Gorbatsevich ◽  
A. S. Moiseenko
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Input Image ◽  
Fine Tuning ◽  
Single Shot ◽  
Face Database ◽  
Face Images ◽  
Face Detection And Recognition ◽  
Number Of Faces ◽  
Detection And Recognition

The paper proposes an architecture and training method of a deep convolutional neural network for simultaneous face detection and recognition. The implemented approach combines the ideas of SSD (Single Shot Detector) and Faster R-CNN (Region proposal Convolutional Neural Networks) algorithms. Face detection is performed similarly to single-stage detection algorithms, and then a biometric template is built by employing RoI (Region of Interest) pooling layers and using the separate branch of the neural network. Training process includes three stages: pretraining of thebasic CNN for face recognition on face images, fine-tuning by using RoI pooling on in painted face images, adding SSD layers and fine-tuning on face detection. Wherein, at the latter stage, training is performed by using shared layers technology for two databases simultaneously. The main feature of the algorithm is high processing speed, which does not depend on the number of faces in the input image. For example, in case of using ResNet-34 as the core architecture for the algorithm, the required time for detecting faces and building biometric templates on an image with 100 faces is less than 13 ms. For training purposes we use CASIA-WebFace for face recognition task and Wider Face for face detection task. Testing is performed on FDDB (Face Detection Dataset and Benchmark), since this database is closer to practical applications than Wider. As long as the main practical task the developed method is intended for is face reidentification, we use Fei Face DataBase for face recognition quality testing. We obtain TPR (True Positive Rate) = 0.928@1000 on FDDB Face DataBase and FAR (Face Acceptance Rate) = 0.03309@FRR (Face Rejection Rate) = 10–4. Therefore, the proposed algorithm allows solving face detection and reidentification tasks in real time with any number of faces on an input image.

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

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