scholarly journals Using Median-LBPH Algorithm for Real-Time Face Recognition System

2021 ◽  
Vol 9 (11) ◽  
pp. 1497-1503
Author(s):  
Lohith Raj S N
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
Real Time ◽  
Web Application ◽  
Computation Time ◽  
Recognition System ◽  
Text Messages ◽  
Law Enforcement Agencies ◽  
Face Expression ◽  
Face Recognition System

Abstract: The LBPH algorithm is used ubiquitously for Face Recognition applications in modern times because of its simplicity of implementation, despite providing high accuracy and less computation time. However, in conditions of varied illumination, face expression and angles at which face images are captured, its confidence is decreased. We propose a slightly modified algorithm that considers the median of the neighbourhood pixels rather than the pixel itself to overcome this issue. This algorithm is called Median-LBPH. The grey value of every pixel is replaced by the median of all the neighbourhood pixel values. Then the features are extracted, and a histogram representing the original image is saved in the model. This model, in turn, can be used to compare with histograms obtained from the faces in real-time footage to find a potential match. This algorithm is used in an end-to-end face recognition system, a web application prototype for Law Enforcement Agencies to maintain a central criminal database shared and accessed across various departments. A live surveillance system is added as part of this novel application so that whenever an already registered criminal appears live on surveillance cameras, a notification will be received, and personnel appropriate Law Enforcement authorities will receive e-mail and text messages through a secured channel. Keywords: Face Recognition, Median-Local Binary Pattern Histogram (MLBPH), Haar Cascade, Adaboost, Neighbourhood Median

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.

Real-time optically processed face-recognition system based on arbitrary moiré contours

1996 ◽  
Author(s):  
Rafael A. Andrade ◽  
Bernard R. Gilbert III ◽  
Donald W. Dawson ◽  
Chris L. Hart ◽  
Samuel P. Kozaitis ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Recognition System ◽  
Face Recognition System

scholarly journals Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Hady Pranoto ◽  
Oktaria Kusumawardani
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Success Factors ◽  
Recognition System ◽  
Expression Recognition ◽  
Student Attendance ◽  
Face Recognition System ◽  
Triplet Loss ◽  
Education Process

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

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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