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.

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 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 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 FDREnet: Face Detection and Recognition Pipeline

2019 ◽  
Vol 9 (2) ◽  
pp. 3933-3938 ◽  
Author(s):  
D. Virmani ◽  
P. Girdhar ◽  
P. Jain ◽  
P. Bamdev
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Accurate Solution ◽  
Facial Features ◽  
Histogram Of Oriented Gradients ◽  
Face Detection And Recognition ◽  
Detection And Recognition

Face detection and recognition are being studied extensively for their vast applications in security, biometrics, healthcare, and marketing. As a step towards presenting an almost accurate solution to the problem in hand, this paper proposes a face detection and face recognition pipeline - face detection and recognition embedNet (FDREnet). The proposed FDREnet involves face detection through histogram of oriented gradients and uses Siamese technique and contrastive loss to train a deep learning architecture (EmbedNet). The approach allows the EmbedNet to learn how to distinguish facial features apart from recognizing them. This flexibility in learning due to contrastive loss accounts for better accuracy than using traditional deep learning losses. The dataset’s embeddings produced from the trained FDREnet result accuracy of 98.03%, 99.57% and 99.39% for face94, face95, and face96 datasets respectively through SVM clustering. Accuracy of 97.83%, 99.57%, and 99.39% was observed for face94, face95, and face96 datasets respectively through KNN clustering.

FACE DETECTION AND RECOGNITION USING MAXIMUM LIKELIHOOD CLASSIFIERS ON GABOR GRAPHS

2009 ◽  
Vol 23 (03) ◽  
pp. 433-461 ◽  
Author(s):  
MANUEL GÜNTHER ◽  
ROLF P. WÜRTZ
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Face Detection ◽  
Recognition System ◽  
Similarity Functions ◽  
Expression Variation ◽  
Face Recognition System ◽  
Face Detection And Recognition ◽  
Ml Estimator ◽  
Detection And Recognition

We present an integrated face recognition system that combines a Maximum Likelihood (ML) estimator with Gabor graphs for face detection under varying scale and in-plane rotation and matching as well as a Bayesian intrapersonal/extrapersonal classifier (BIC) on graph similarities for face recognition. We have tested a variety of similarity functions and achieved verification rates (at FAR 0.1%) of 90.5% on expression-variation and 95.8% on size-varying frontal images within the CAS-PEAL database. Performing Experiment 1 of FRGC ver2.0, the method achieved a verification rate of 72%.

scholarly journals Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Di Lu ◽  
Limin Yan
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Data Communication ◽  
Electrical Signal ◽  
Network Technology ◽  
Skin Texture ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
Detection And Recognition

With the continuous innovation of network technology, various kinds of convenient network technologies have grown, and human dependence on network technology has gradually increased, which has resulted in the importance of network information security issues. With the continuous development of my country’s industrialization, the application of sensors is becoming more and more extensive, for example, the security vulnerabilities and defects in the operating system itself. Traditional sensors can “perceive” a certain thing or signal, convert it into an electrical signal and record it, and then use a conversion circuit to output the electrical signal into a value or other display form that is conducive to observation. Nowadays, sensors have been further developed. Based on the original “perception” function, combined with computer technology, it integrates data storage, data processing, data communication, and other functions, so that it has analysis functions and can better display information. The technical level has reached a new level. Early intelligent recognition mainly used the uniqueness of finger and palm lines to scan and contrast, but due to some weather reasons or skin texture constraints caused by skin texture, these methods showed certain limitations. This paper proposes a new computer vision-based algorithm from face detection technology and face recognition technology. In the face detection technology, it is mainly introduced from the OpenCV method. Face recognition technology is improved in practical applications through the Seetaface method and YouTu method. At the same time, using the contrast experiment, the detection and recognition rates under the three different requirements of side face detection, occlusion detection, and facial exaggerated expression are compared, and the accuracy of each method is improved. The results show that each case is compared in each case. The advantages and disadvantages of the algorithm effectively verify the effectiveness of the method.

scholarly journals Masked Face Analysis via Multi-Task Deep Learning

2021 ◽  
Vol 7 (10) ◽  
pp. 204
Author(s):  
Vatsa S. Patel ◽  
Zhongliang Nie ◽  
Trung-Nghia Le ◽  
Tam V. Nguyen
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Mask ◽  
Learning Model ◽  
Unified Framework ◽  
Improve Performance ◽  
Face Analysis ◽  
And Gender ◽  
Deep Learning Model

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

OBJECT DETECTION USING VIOLA-JONES ALGORITHM

2018 ◽  
Vol 28 (4) ◽  
pp. 1349-1354
Author(s):  
Dušan Stevanović
Keyword(s):  
Face Detection ◽  
Three Dimensional ◽  
Human Head ◽  
Accurate Information ◽  
Feedback Information ◽  
Three Dimensional Objects ◽  
The Face ◽  
Face Detection And Recognition ◽  
False Recognitions ◽  
Detection And Recognition

In this paper it has been described and applied method for detecting face and face parts in images using the Viola-Jones algorithm. The work is based on Computer Vision Systems, artificial intelligence that deals with the recognition of two-dimensional or three-dimensional objects. When Cascade Object Detector script is trained, multimedia content is assigned for recognition. In this work the content will be in the form of an image, where the program will have the task of recognizing the objects in the images, separating the parts of the images in the head area, and on each discovered face, separately mark the area around the eyes, nose and mouth.Algorithm for detection and recognition is based on scanning and analyzing front part of human head. Common usage of face detection and recognition can be find in biometry, photography, on autofocus option which is implemented in professional photo cameras or on smiling detectors (Keller, 2007). Marketing is also popular field where face detection and recognition can be used. For example, web cameras that are implemented in TVs, can detect every face in near area. Calculating different type of algorithms and parameters, based on sex, age, ethnicity, system can play precisely segmented television commercials and campaigns. Example of that kind of systems is OptimEyes. (Strasburger, 2013)In other words, every algorithm that has as its main goal to detect and recognize face from image, should give as a feedback information, is there any face and if answer is positive, where is its location on image. In order to achieve acceptable performances, algorithm should minimize false recognitions. These are the cases when the algorithm ignores and does not recognize the real object from the image, and vice versa, when the wrong object is recognized as real. One of the algorithms that is frequently applied in this area of research is the Viola-Jones algorithm. This algorithm is functional in real time, meaning that besides detection, it is also possible to adjust the ability to monitor faces from video material.In this paper, the problem that will be analyzed is facial image detection. Man can do this task in a very simple way, but to do the same with a computer, it is necessary to have a range of precise and accurate information, formulas, methods and techniques. In order to maximize the precision of recognizing the face of the image using the Viola-Jones algorithm, it is desirable that the objects in the images are completely face-to-face with the image-taking device, which will be shown through experiments.

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.

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