Regression-Based Automated Facial Image Quality Model

2017 ◽  
Vol 11 (4) ◽  
pp. 22-40 ◽  
Author(s):  
Fatema Tuz Zohra ◽  
Andrei D. Gavrilov ◽  
Omar A. Zatarain ◽  
Marina L. Gavrilova
Keyword(s):  
Face Recognition ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Analysis ◽  
Recognition System ◽  
Quality Factors ◽  
Quality Estimation ◽  
Face Recognition System ◽  
The Impact

Nowadays, biometric technologies became reliable and widespread means of unobtrusive user authentication in a variety of real-world applications. The performance of an automated face recognition system has a strong relationship with the quality of the biometric samples. The facial samples can be affected by various quality factors, such as uneven illumination, low or high contrast, excessive brightness, blurriness, etc. In this article, the authors propose a quality estimation method based on linear regression analysis to characterize the relationship between different quality factors and the performance of a face recognition system. The regression model can predict the overall quality of a facial sample which reflects the effects of various quality factors on that sample. The weights assigned to the different quality factors by the linear regression model reflect the impact of those quality factors on the performance of the recognition system. Therefore, the prediction scores generated from the model is a strong indicator of the overall quality of the facial images. The authors evaluated the quality estimation model on the Extended Yale Database B. They also performed a study to understand which quality factors affect the face recognition the most.

Quality Estimation for Facial Biometrics

2020 ◽  
pp. 298-320
Author(s):  
Sanjida Nasreen Tumpa ◽  
Andrei Dmitri Gavrilov ◽  
Omar Zatarain Duran ◽  
Fatema Tuz Zohra ◽  
Marina L. Gavrilova
Keyword(s):  
Face Recognition ◽  
Facial Recognition ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Recognition System ◽  
Quality Factors ◽  
Quality Estimation ◽  
Video Frames ◽  
Face Recognition System

Over past decade, behavioral biometric systems based on face recognition became leading commercial systems that meet the need for fast and efficient confirmation of a person's identity. Facial recognition works on biometric samples, like image or video frames, to recognize people. The performance of an automated face recognition system has a strong relationship with the quality of the biometric samples. In this chapter, the authors propose a quality estimation method based on a linear regression analysis to characterize the relationship between different quality factors and the performance of a face recognition system. The regression model can predict the overall quality of a facial sample which reflects the effects of various quality factors on that sample. The authors evaluated the quality estimation model on the Extended Yale Database B, finally formulating a data set of samples which will enable efficient implementation of biometric facial recognition.

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 COVID 19: THE IMPACT OF DEMAND, SUPPLY, AND LABOR

2020 ◽  
Vol 4 (5) ◽  
pp. 60-73
Author(s):  
Hantono
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Data Collection ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Independent Variables ◽  
The Impact

This study aims to determine the effect of 1) demand, 2) supply, 3) labor, 4) covid 19. The sampling in this research was conducted by using a incidental sampling method. Methods of data collection through questionnaires that have been distributed to 100 respondents who have met criteria. With multiple linear regression analysis, it shows that the demand, supply, labor both partial and simultaneous have significant effect on covid 19. It can be concluded that mitigation of demand, supply, labor towards covid 19. The results of t test showed that demand is approved and indicates demand has great impact on affecting the covid 19, supply is not approved and indicates supply has less impact on affecting the covid 19, labor is approved and indicates labor has great impact on affecting the covid 19. The results of f test showed that both of the independent variables are simultaneously affecting the covid 19. The result of R Square of the regression model is 0.216 which shows that 21,6 % of mitigation of covid 19 can be explained by demand, supply, labor. Whereas, the 78,4% of covid 19 variable can be explained by other factors or variables which are not examined in this research.

scholarly journals Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Abdulbasit Alazzawi ◽  
Osman N. Ucan ◽  
Oguz Bayat
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Linear Regression ◽  
Edge Detection ◽  
Extraction Method ◽  
Principal Component ◽  
Recognition System ◽  
Detection Techniques ◽  
Face Recognition System ◽  
Recognition Systems

Recent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient-and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.

scholarly journals Comparative analysis of face recognition systems built using standard architecture blocks

Telecom IT ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 94-101
Author(s):  
E. Kalyashov
Keyword(s):  
Face Recognition ◽  
Comparative Analysis ◽  
Original Data ◽  
Recognition System ◽  
Practical Relevance ◽  
Research Subject ◽  
Face Recognition System ◽  
Recognition Quality ◽  
Recognition Systems

Research subject. The article reviews ways of constructing face recognition systems based on standard modules. Method. The study is based on comparison of performance and recognition quality of various pipelines. Core results. Values of reached recognition quality and dependencies from a type of original data are presented. Practical relevance. The results could be used while implementing various face recognition system pipelines.

scholarly journals Proposing an Image Enhancement Algorithm Using CNN for Applications of Face Recognition System

2020 ◽  
pp. 1-14
Author(s):  
Phat Nguyen Huu ◽  
Loc Hoang Bao ◽  
Hoang Lai The
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Face Recognition ◽  
Object Recognition ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Extreme Weather Events ◽  
Face Recognition System

Many researches have been going on since last two decades for object recognition, shape matching, and pattern recognition in the field of computer vision. Face recognition is one of the important issues in object recognition and computer vision. Many face image datasets, related competitions, and evaluation programs have encouraged innovation, producing more powerful facial recognition technology with promising results. In recent years, we have witnessed tremendous improvements in face recognition performance from complex deep neural network architectures trained on millions of face images. Face recognition is the most important biometric and stills many challenges such as pose variation, illumination variation, etc. In order to achieve the desired performance when deploying in reality, the methods depend on many factors. One of the main factors is quality of input image. Therefore, facial recognition systems is installed outdoors which are always affected by extreme weather events such as haze, fog. The existence of haze dramatically degrades the visibility of outdoor images captured in inclement weather and affects many high-level computer vision tasks such as detection and recognition system. In this paper, we propose a preprocessing method to remove haze from input images that enhances their quality to improve effectiveness and recognition rate for face identification based on Convolutional Neural Network (CNN) based on the available datasets and our self-built data. To perform the proposed method for outdoor face recognition system, we have improved the system accuracy from 90.53% to 98.14%. The results show that the proposed method improves the quality of the image with other traditional methods.

scholarly journals Face Recognition of Passenger for Bus Services

2021 ◽  
Author(s):  
Mohammad Azerul Azlan ◽  
◽  
Abd Kadir Mahamad ◽  
Sharifah Saon ◽  
◽  
...  
Keyword(s):  
Face Recognition ◽  
Internet Of Things ◽  
University Students ◽  
Recognition System ◽  
Raspberry Pi ◽  
Counting System ◽  
Face Classification ◽  
Face Recognition System ◽  
Bus Services

Most university students are using the bus provided by the university's management to move from one place to another place. The analysis are required to improvise the quality of the of bus services such as the amount of passenger that using the bus and information of passengers such as gender. The objectives of this project are to develop face recognition system based on gender using Raspberry Pi 4 and Intel Neural Compute Stick 2 and to test and validate the performance of the developed system for face classification and passenger counting system. Also this system is able to store passenger information into Google Firebase Cloud with Internet of Things. This system is used Raspbian in Raspberry Pi 4 with the libraries that used for face classification and recognition such as OpenCV and OpenVINO. This project able to detect faces of the passengers soon as they ride the bus and determine gender of the passengers and count passengers according gender and the information of the passengers will stored in Google Firebase. There are some recommendation that need to be added in this project to improve efficiency of the system.

scholarly journals Researching a machine learning algorithm for a face recognition system

2021 ◽  
pp. 10-12
Author(s):  
Serhii Yevseiev ◽  
Anna Goloskokova ◽  
Olexander Shmatko
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Video Sequence ◽  
Learning Algorithm ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Human Face Recognition ◽  
Face Recognition System ◽  
Digital Fingerprint ◽  
The Impact

This article investigated the problem of using machine learning algorithms to recognize and identify a user in a video sequence. The scientific novelty lies in the proposed improved Viola-Jones method, which will allow more efficient and faster recognition of a person's face. The practical value of the results obtained in the work is determined by the possibility of using the proposed method to create systems for human face recognition. A review of existing methods of face recognition, their main characteristics, architecture and features was carried out. Based on the study of methods and algorithms for finding faces in images, the Viola-Jones method, wavelet transform and the method of principal components were chosen. These methods are among the best in terms of the ratio of recognition efficiency and work speed. Possible modifications of the Viola-Jones method are presented. The main contribution presented in this article is an experimental study of the impact of various types of noise and the improvement of company security through the development of a computer system for recognizing and identifying users in a video sequence. During the study, the following tasks were solved: – a model of face recognition is proposed, that is, the system automatically detects a person's face in the image (scanned photos or video materials); – an algorithm for analyzing a face is proposed, that is, a representation of a person's face in the form of 68 modal points; – an algorithm for creating a digital fingerprint of a face, which converts the results of facial analysis into a digital code; – development of a match search module, that is, the module compares the faceprint with the database until a match is found

scholarly journals Impact of Goods Quality and Advertising on Buying Decision of Gembira Bread Brand

2020 ◽  
Vol 10 (4) ◽  
pp. 26
Author(s):  
Rakhmawati Purba ◽  
Aisyah Siregar ◽  
Rusma Wehni
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Primary Data ◽  
Analysis Method ◽  
Independent Variable ◽  
Quantitative Descriptive ◽  
The Impact

The purpose of this study was to determine the impact of the quality of goods and advertisements on the decision to buy Gembira bread brand. The independent variable of this study consists of the quality of goods and advertisements, while the dependent variable is the buying decision. This research is quantitative descriptive. The research was conducted on the community in the Padang Hilir District, Tebing Tinggi City. The data used are primary data obtained through questionnaire answers from 50 respondents. Data were analyzed using multiple linear regression analysis method. The results showed that the quality of the goods had a significant impact on buying decisions. Likewise, advertising has a significant impact on Buying Decision. For further researchers, it is hoped that they can examine other factors that are not revealed in this study.

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