Cancelable Biometric System for face Recognition Based on a Regularized Restoration Model

2021 ◽  
Author(s):  
Abdel-Aziz I. M. Hassanin ◽  
Fathi E. Abd El-Samie ◽  
Abd El-hamid Mohamed
Keyword(s):  
Face Recognition ◽  
Biometric System ◽  
Restoration Model

scholarly journals Design of a Face Recognition System based on Convolutional Neural Network (CNN)

2020 ◽  
Vol 10 (3) ◽  
pp. 5608-5612 ◽  
Author(s):  
Y. Said ◽  
M. Barr ◽  
H. E. Ahmed
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition System ◽  
Great Variability ◽  
Artificial Vision ◽  
Surveillance Systems ◽  
Distributed Network ◽  
Biometric System ◽  
Face Recognition System ◽  
Deep Learning Model

Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.

scholarly journals Face Detection and Gender Classification using Facial Features

2019 ◽  
Vol 8 (4) ◽  
pp. 6670-6674
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Facial Features ◽  
Gender Classification ◽  
Biometric System ◽  
Detection Techniques ◽  
The Face ◽  
And Gender ◽  
Two Stages

Face Recognition is the most important part to identifying people in biometric system. It is the most usable biometric system. This paper focuses on human face recognition by calculating the facial features present in the image and recognizing the person using features. In every face recognition system follows the preprocessing, face detection techniques. In this paper mainly focused on Face detection and gender classification. They are performed in two stages, the first stage is face detection using an enhanced viola jones algorithm and the next stage is gender classification. Input to the video or surveillance that video converted into frames. Select few best frames from the video for detecting the face, before the particular image preprocessed using PSNR. After preprocessing face detection performed, and gender classification comparative analysis done by using a neural network classifier and LBP based classifier

A Review on Biometric Technology

2018 ◽  
Vol 8 (4) ◽  
pp. 101
Author(s):  
Kartik Choudhary ◽  
Rizwan Khan
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Review Paper ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Biometric Technology ◽  
The Face ◽  
The Individual ◽  
Recognition Technique ◽  
Better Than

Biometric Technology has turned out to be a popular area of research in computer vision and one of the most successful applications for identifying humans by capturing and analysing the sole feature or characteristic of   individual which is possessed by them and involves their Physical and Behavioral characteristics. For the individual validation and authentication the biometric system has this responsibility. Biometric Technology started from the fingerprints recognition and later on improvements were done in it to make it more secure which involves the face recognition and iris Recognition. Almost both of them are available and regarded as the accurate and reliable technology for biometric validation system. This review paper is all about Face recognition techniques in biometric locking system and Iris recognition technique of identification and the ways of making locking systems ways more efficient, full of ease, more secure, and far better than before so as to make locking or security stronger. It discusses about face recognition technique, its working and its application in different sector along with iris recognition, its working, its application.

scholarly journals Combined PCA-Daugman Method : An Effcient Technique for Face and Iris Recognition

2020 ◽  
pp. 34-44
Author(s):  
Md. Mahbubul Alam ◽  
Md. Ashikur Rahman Khan ◽  
Zayed Us Salehin ◽  
Main Uddin ◽  
Sultana Jahan Soheli ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Iris Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Biometric System ◽  
Recognition Method ◽  
Person Identification ◽  
Multimodal Biometric System ◽  
Recognition Efficiency

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.

scholarly journals A Review on Face Anti-Spoofing

2021 ◽  
Vol 5 (1) ◽  
pp. 29
Author(s):  
Rizky Naufal Perdana ◽  
Igi Ardiyanto ◽  
Hanung Adi Nugroho
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Real World ◽  
Biometric System ◽  
Security Issue ◽  
Security Technology ◽  
The Face ◽  
Paper Review ◽  
Recognition Systems ◽  
Face Spoofing

The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.

scholarly journals Biometrics Systems and Technologies: A survey

2016 ◽  
Vol 11 (3) ◽  
pp. 315 ◽  
Author(s):  
Ioan Buciu ◽  
Alexandru Gacsadi
Keyword(s):  
Face Recognition ◽  
Human Computer Interaction ◽  
Biometric System ◽  
Interested Reader ◽  
Biometric Security ◽  
Detailed List ◽  
Biometric Templates ◽  
Password Based Authentication ◽  
Biometric Features ◽  
Biometric Based Authentication

In a nutshell, a biometric security system requires a user to provide some biometric features which are then verified against some stored biometric templates. Nowadays, the traditional password based authentication method tends to be replaced by advanced biometrics technologies. Biometric based authentication is becoming increasingly appealing and common for most of the human-computer interaction devices. To give only one recent example, Microsoft augmented its brand new Windows 10 OS version with the capability of supporting face recognition when the user login in. This chapter does not intend to cover a comprehensive and detailed list of biometric techniques. The chapter rather aims at briefly discussing biometric related items, including principles, definitions, biometric modalities and technologies along with their advantages, disadvantages or limitations, and biometric standards, targeting unfamiliar readers. It also mentions the attributes of a biometric system as well as attacks on biometrics. Important reference sources are pointed out so that the interested reader may gain deeper in-depth knowledge by consulting them.

scholarly journals A Comparative Review on Different Methods of Face Recognition

2017 ◽  
Vol 10 (1) ◽  
pp. 227-231
Author(s):  
Tenzin Dawa ◽  
N Vijayalakshmi
Keyword(s):  
Face Recognition ◽  
Matrix Factorization ◽  
Recognition System ◽  
Support Vector ◽  
Facial Features ◽  
Biometric System ◽  
Local Ternary Pattern ◽  
Face Recognition System ◽  
Comparative Review ◽  
Non Negative Matrix Factorization

Face Recognition is a biometric system which can be used to identify or verify a person from digital image by using the facial features that are unique to each other. There are many techniques which can be used in a face recognition system. In this paper we review some of the algorithms and compare them to see which technique is better compared to one another. Techniques that are compared in this technique are Non-negative matrix factorization (NMF) with Support Vector Machine (SVM), Partial Least Squares (PLS) with Hidden Markov Model (HMM) and Local Ternary Pattern (LTP) with Booth’s Algorithm.

scholarly journals Impact on Biometric Identification Systems of COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuheng Guo
Keyword(s):  
Face Recognition ◽  
Comprehensive Evaluation ◽  
Biometric Identification ◽  
Biometric System ◽  
Biometric Systems ◽  
Face Detection And Recognition ◽  
Considerable Impact ◽  
Recognition Systems ◽  
The Impact ◽  
Detection And Recognition

COVID-19 has had an inevitable impact on the daily life of people in 2020. Changes in behavior such as wearing masks have a considerable impact on biometric systems, especially face recognition systems. When people are aware of this impact, a comprehensive evaluation of this phenomenon is lacking. The purpose of this paper is to qualitatively evaluate the impact of COVID-19 on various biometric systems and to quantitatively evaluate face detection and recognition. The experimental results show that a real-world masked face dataset is essential to build an effective face recognition-based biometric system.

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