scholarly journals Real time Face Detection and Optimal Face Mapping for Online Classes

2022 ◽  
Vol 2161 (1) ◽  
pp. 012063
Author(s):  
MCP Archana ◽  
CK Nitish ◽  
Sandhya Harikumar
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Online Classes ◽  
Web Based ◽  
Online Classrooms ◽  
Face Detection And Recognition ◽  
Haar Cascade ◽  
Improved Accuracy

Abstract The main objective of this paper is to provide a web-based tool for identifying faces in a real-time environment, such as Online Classes. Face recognition in real-time is now a fascinating field with an ever-increasing challenge such as light variations, occlusion, variation in facial expressions, etc. During the current pandemic scenario of COVID-19, the demand for online classrooms has rapidly increased. This has escalated the need for a real-time, economic, simple, and convenient way to track the attendance of the students in a live classroom. This paper addresses the aforementioned issue by proposing a real-time online attendance system. Two alternative face recognition algorithms are perceived in order to develop the tool for realtime face detection and recognition with improved accuracy. The algorithms adopted are Local Binary Pattern Histogram(LBPH) and Convolutional Neural Network (CNN) for face recognition as well as Haar cascade classifier with boosting for face detection. Experimental results show that CNN with an accuracy of 95% is better in this context than LBPH that yields an accuracy of 78%.

Face Recognition and Face Detection Using Open Computer Vision Classifiers and Python

2021 ◽  
pp. 185-207
Author(s):  
Priyank Jain ◽  
Meenu Chawla ◽  
Sanskar Sahu
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Research Work ◽  
Modern World ◽  
The Face ◽  
Haar Cascade

Identification of a person by looking at the image is really a topic of interest in this modern world. There are many different ways by which this can be achieved. This research work describes various technologies available in the open-computer-vision (OpenCV) library and methodology to implement them using Python. To detect the face Haar Cascade are used, and for the recognition of face eigenfaces, fisherfaces, and local binary pattern, histograms has been used. Also, the results shown are followed by a discussion of encountered challenges and also the solution of the challenges.

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 Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

2021 ◽  
Vol 11 (1) ◽  
pp. 45-58
Author(s):  
R. Rizal Isnanto ◽  
Adian Rochim ◽  
Dania Eridani ◽  
Guntur Cahyono
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Video Data ◽  
Cascade Classifier ◽  
Low Resolution ◽  
The Face ◽  
Haar Cascade ◽  
Pattern Histogram ◽  
Low Resolution Images

This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.

scholarly journals Software Development Framework for Real-Time Face Detection and Recognition in Mobile Devices

2020 ◽  
Vol 14 (04) ◽  
pp. 103
Author(s):  
Laxmisha Rai ◽  
Zhiyuan Wang ◽  
Amila Rodrigo ◽  
Zhaopeng Deng ◽  
Haiqing Liu
Keyword(s):  
Face Recognition ◽  
Software Development ◽  
Real Time ◽  
Mobile Devices ◽  
Face Detection ◽  
Scale Transformation ◽  
Development Framework ◽  
The Face ◽  
Face Detection And Recognition ◽  
Detection And Recognition

With the rapid use of Android OS in mobile devices and related products, face recognition technology is an essential feature, so that mobile devices have a strong personal identity authentication. In this paper, we propose Android based software development framework for real-time face detection and recognition using OpenCV library, which is applicable in several mobile applications. Initially, the Gaussian smoothing and gray-scale transformation algorithm is applied to preprocess the source image. Then, the Haar-like feature matching method is used to describe the characteristics of the operator and obtain the face characteristic value. Finally, the normalization method is used to match the recognition of face database. To achieve the face recognition in the Android platform, JNI (Java Native Interface) is used to call the local Open CV. The proposed system is tested in real-time in two different brands of smart phones, and results average success rate in both devices for face detection and recognition is 95% and 80% respectively.

scholarly journals A web-based application for face detection in real-time images and videos

2022 ◽  
Vol 2161 (1) ◽  
pp. 012071
Author(s):  
Mehul Arora ◽  
Sarthak Naithani ◽  
Anu Shaju Areeckal
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Face Detection ◽  
User Interfaces ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Web Based ◽  
Detection Algorithms ◽  
Haar Cascade ◽  
The Web

Abstract Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.

A Real-Time Face Recognition System for Android Smart Phone

2013 ◽  
Vol 756-759 ◽  
pp. 4006-4010 ◽  
Author(s):  
Gang Yang ◽  
Jia Ni Luo
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Smart Phone ◽  
Recognition System ◽  
Adaboost Algorithm ◽  
Face Recognition System ◽  
Android Platform ◽  
Face Detection And Recognition ◽  
Detection And Recognition

With the widely application of face recognition and the rapid development of Android OS, technique of face detection and recognition based on Android platform becomes increasingly attractive. This paper presents a real-time face recognition system on Android platform. The system realizes face detection by applying AdaBoost algorithm and face recognition by utilizing Eigenfaces. This paper also came up with some methods to speed up the face detection and recognition process and improve the correct rate of face recognition. Experimental results show that this system is able to realize real-time face detection and recognition on Android smart phones. In addition, all the work is completed on the smart phone without using any other terminals or tools.

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.

A Real-time Face Recognition Smart Attendance System with Haar Cascade Classifiers

2021 ◽  
Author(s):  
Rupak Bairagi ◽  
Remon Ahmed ◽  
Sadia Afrin Tisha ◽  
Md. Sumon Sarder ◽  
Md. Sabiqul Islam ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Cascade Classifiers ◽  
Haar Cascade ◽  
Haar Cascade Classifiers
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