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.

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.

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 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 Web-Based Mobile Attendance System with Facial Recognition Feature

2020 ◽  
Vol 14 (05) ◽  
pp. 193
Author(s):  
Noradila Nordin ◽  
Nurul Husna Mohd Fauzi
Keyword(s):  
Face Recognition ◽  
Mobile Devices ◽  
Conventional Method ◽  
Facial Recognition ◽  
Recognition System ◽  
Web Based ◽  
Face Recognition System ◽  
The Face ◽  
System Prototype

Attendance marking in a classroom is one of the methods used to track the student’s presence in the lecture. The conventional method that is being enforced has shown to be vulnerable, inaccurate and time-consuming especially in a large classroom. It is difficult to identify absentees and proxy attendees based on the conventional attendance marking method. In order to overcome the challenges faced in the conventional method, a web-based mobile attendance system with facial recognition feature is proposed. It incorporated the existing mobile devices with a camera and the face recognition system to allow the attendance system to be used in classrooms automatically and efficiently with minor implementation requirements. The system prototype received positive responses from the volunteers who tested the system to replace the conventional attendance marking.

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 Design of Facial Recognition System Based on Visual Communication Effect

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xuhui Fu
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Visual Communication ◽  
Facial Recognition ◽  
Recognition System ◽  
Image Capture ◽  
Hardware System ◽  
Face Recognition System ◽  
The Face ◽  
Facial Recognition System

At present, facial recognition technology is a very cutting-edge science and technology, and it has now become a very hot research branch. In this research, first, the thesis first summarized the research status of facial recognition technology and related technologies based on visual communication and then used the OpenCV open source vision library based on the design of the system architecture and the installed system hardware conditions. The face detection program and the image matching program are realized, and the complete face recognition system based on OpenCV is realized. The experimental results show that the hardware system built by the software can realize the image capture and online recognition. The applied objects are testers. In general, the OpenCV-based face recognition system for testers can reliably, stably, and quickly realize face detection and recognition in this situation. Facial recognition works well.

scholarly journals Multiscale Retinex Application to Analyze Face Recognition

2020 ◽  
Vol 5 (2) ◽  
pp. 217
Author(s):  
Supriyanto Supriyanto ◽  
Maisevli Harika ◽  
Maya Sri Ramadiani ◽  
Diena Rauda Ramdania
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Facial Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Facial Image ◽  
Main Challenge ◽  
Face Recognition System

The main challenge that facial recognition introduces is the difficulty of uneven lighting or dark tendencies. The image is poorly lit, which makes it difficult for the system to perform facial recognition. This study aims to normalize the lighting in the image using the Multiscale Retinex method. This method is applied to a face recognition system based on Principal Component Analysis to determine whether this method effectively improves images with uneven lighting. The results showed that the Multiscale Retinex approach to face recognition's correctness was better, from 40% to 76%. Multiscale Retinex has the advantage of dark facial image types because it produces a brighter image output.

scholarly journals TESTING OF MODULES FOR FACIAL RECOGNITION

2020 ◽  
pp. 132-136
Author(s):  
N.Ramya ◽  
D.Manasa ◽  
N.Ramya Sri ◽  
Sk.Naveed
Keyword(s):  
Face Recognition ◽  
Human Body ◽  
Facial Recognition ◽  
Recognition System ◽  
Input Image ◽  
Image Encoding ◽  
Face Recognition System ◽  
The Face ◽  
Crucial Part

Face is the crucial part of the human body that uniquely identifies a person. Using the face characteristics as biometric, the face recognition system can be implemented. The most demanding task in any organization is attendance marking. In traditional attendance system, the students are called out by the teachers and their presence or absence is marked accordingly. However, these traditional techniques are time consuming and tedious. In this project, the Open CV based face recognition approach has been proposed. This model integrates a camera that captures an input image, an algorithm for detecting face from an input image, encoding and identifying the face, marking the attendance in a spreadsheet and converting it into PDF file. The training database is created by training the system with the faces of the authorized students. The cropped images are then stored as a database with respective labels. The features are extracted using LBPH algorithm.

scholarly journals Lecturer Attendance System using Face Recognition Application an Android-Based

2021 ◽  
Vol 3 (2) ◽  
pp. 167-173
Author(s):  
Feri Susanto ◽  
Fauziah Fauziah ◽  
Andrianingsih Andrianingsih
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Facial Recognition ◽  
Recognition System ◽  
Security Systems ◽  
Technological Advances ◽  
Face Recognition System ◽  
The Face ◽  
Operational Activities ◽  
Pattern Histogram

In the field of industries, businesses, and offices the use of security systems and administrative management through data input using a face recognition system is being developed. Following the era of technological advances, communication and information systems are widely used in various administrative operational activities and company security systems because it is assessed by using a system that is based on facial recognition security levels and more secure data accuracy, the use of such systems is considered to have its characteristics so it is very difficult for other parties to be able to engineer and manipulate data produced as a tool to support the company's decision. Related to this, causing the author is to try to research the detection of facial recognition that is present in the application system through an Android device, then face recognition detection will be connected. and saved to the database that will be used as data about the presence of teaching lecturers. Using the local binary pattern histogram algorithm method to measure the face recognition system that can be applied as a technique in the attendance system of lecturers to be more effective and efficient. Based on testing by analyzing the false rate error rate and the false refusal rate can be seen that the average level of local binary pattern histogram accuracy reaches 95.71% better than through the Eigenface method which is equal to 76.28%.

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.

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