scholarly journals Face Recognition Attendance System

2022 ◽  
Vol 10 (1) ◽  
pp. 280-283
Author(s):  
Shrey Bhagat
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Digital Age ◽  
Invasive Technique ◽  
Live Streaming ◽  
Database Development ◽  
Non Invasive ◽  
Development Face ◽  
Security Authentication ◽  
Recognition Systems

Abstract: Face recognition systems are used in practically every industry in this digital age. One of the most widely utilized biometrics is face recognition. It can be used for security, authentication, and identity, among other things. Despite its low accuracy relative to iris and fingerprint identification, it is extensively utilized because it is a contactless and non-invasive technique. Face recognition systems can also be used to track attendance in schools, colleges, and companies. Because the existing manual attendance system is time consuming and difficult to maintain, this system intends to create a class attendance system that employs the concept of face recognition. There’s also the possibility of proxy attendance. As a result, the demand for this system grows. Database development, face detection, face recognition, and attendance updating are the four steps of this system. The photos of the kids in class are used to generate the database. Faces are discovered and recognized from the classroom's live streaming footage. At the end of the session, the attendance will be mailed to the appropriate faculty. Keywords: Smart Attendance System, NFC, RFID, OpenCV, NumPy

A Few Approaches To Face Detection In Face Recognition Systems

2002 ◽  
pp. 313-322
Author(s):  
Georgi Koukharev ◽  
Tomasz Ponikowski ◽  
Liming Chen
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition Systems

scholarly journals DTFA-Net: Dynamic and Texture Features Fusion Attention Network for Face Antispoofing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xin Cheng ◽  
Hongfei Wang ◽  
Jingmei Zhou ◽  
Hui Chang ◽  
Xiangmo Zhao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Feature Fusion ◽  
Texture Features ◽  
Replay Attack ◽  
Attention Network ◽  
Detection Failure ◽  
The Face ◽  
Recognition Systems ◽  
Common Face

For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

scholarly journals Face Detection & Recognition using Tensor Flow: A Review

2018 ◽  
Vol 18 ◽  
pp. 7381-7388
Author(s):  
Ishaan Chawla
Keyword(s):  
Social Media ◽  
Face Recognition ◽  
Access Control ◽  
Face Detection ◽  
Facial Recognition ◽  
Rapid Development ◽  
Standard Form ◽  
Surveillance Systems ◽  
Security Systems ◽  
Recognition Systems

Face recognition has become a popular topic of research recently due to increases in demand for security as well as the rapid development of mobile devices. There are many applications which face recognition can be applied to such as access control, identity verification, security systems, surveillance systems, and social media networks. Access control includes offices, computers, phones, ATMs, etc. Most of these forms currently do not use face recognition as the standard form of granting entry, but with advancing technologies in computers along with more refined algorithms, facial recognition is gaining some traction in replacing passwords and fingerprint scanners. Ever since the events of 9/11 there has been a more concerned emphasis on developing security systems to ensure the safety of innocent citizens. Namely in places such as airports and border crossings where identification verification is necessary, face recognition systems potentially have the ability to mitigate the risk and ultimately prevent future attacks from occurring. As for surveillance systems, the same point can be made if there are criminals on the loose. Surveillance cameras with face recognition abilities can aide in efforts of finding these individuals. Alternatively, these same surveillance systems can also help identify the whereabouts of missing persons, although this is dependent on robust facial recognition algorithms as well as a fully developed database of faces. And lastly, facial recognition has surfaced in social media applications on platforms such as Facebook which suggest users to tag friends who have been identified in pictures. It is clear that there are many applications the uses for facial recognition systems. In general, the steps to achieve this are the following: face detection, feature extraction, and lastly training a model.

scholarly journals Face Recognition Attendance System using Raspberry Pi

2021 ◽  
Vol 9 (8) ◽  
pp. 1145-1149
Author(s):  
Tresnor Menezes
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Face Recognition ◽  
Network Models ◽  
Raspberry Pi ◽  
Biometric Authentication ◽  
Neural Network Models ◽  
Social Media Networks ◽  
Non Invasive ◽  
Security Authentication

Abstract: Face recognition is used for security, authentication, identification, and has got many advantages over conventional methods. It is being used in many sectors since it is contactless and non-invasive. Billions of images have been uploaded on social media networks and are crawled by search engines over many years. These images may include many different faces. The increase in computing capability and collected data has helped in creating more powerful neural network models. [1] This project thesis aims to create an attendance system which uses face recognition biometric authentication as the currently used manual attendance system is cumbersome to maintain and time consuming. Face recognition prohibits the chance of students marking attendance for their peers (proxy attendance). Keywords: Face Recognition, Face embeddings, Face Detection, Image Processing, Raspberry Pi automation.

USING DOPPLER TO ASSESS THE FETAL HEALTH FOR THE PREDICTION OF GESTATIONAL OUTCOME

2014 ◽  
pp. 9-18
Author(s):  
Thi Linh Giang Truong ◽  
Vu Quoc Huy Nguyen
Keyword(s):  
Middle Cerebral Artery ◽  
Cerebral Artery ◽  
Umbilical Artery ◽  
Functional Method ◽  
Invasive Technique ◽  
Routine Practice ◽  
Fetal Health ◽  
Uterine Arteries ◽  
Non Invasive ◽  
Key Words Doppler

Background: Assessment of fetal health plays the most important role in prenatal care because of influence of the prediction of gestational outcome. One of the main aims of routine antenatal care is to identify the ‘ at risk ‘ fetus in order to apply clinical interventions which could results in reduced perinatal morbidity and mortality. Doppler ultrasound is a non invasive technique whereby the movement of blood is studied by detecting the change in frequence of reflected sound, Doppler blood flow velocity waves form of fetal side (umbilical artery, middle cerebral artery ...) and maternal side ( uterine arteries) are discussed and monograms for routine practice are presented. Recently this method is important tool for qualifying high risk pregnancies and help early forecasts the health of the babies and mothers disorder. Doppler sonography in obstetrics is a widely accepted functional method of examining the prediction of gestational outcome. Key words: Doppler, umbilical artery, middle cerebral artery, uterine arteries

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