scholarly journals Diagnostic station for a multibiometric system for a selected transport object

2018 ◽  
Vol 19 (12) ◽  
pp. 585-588
Author(s):  
Jacek Paś
Keyword(s):  
Biometric System ◽  
Security Systems ◽  
Railway Stations ◽  
Biometric Systems ◽  
Diagnostic Position ◽  
Identification Techniques ◽  
Face Biometric

Multibiometric systems used in transport objects, in contradistinction to "ordinary" biometric systems, use several recognition techniques, e.g. fingerprint, iris, voice or face. Biometric devices are sometimes part of electronic security systems. These systems are currently installed in many transport facilities - stationary and non-stationary where there is a lot of personal traffic. These devices are most often used in extensive areas, airports, logistics bases or railway stations. The article presents issues concerning the diagnostic position for a biometric system which has in its structure several simple identification techniques.

scholarly journals EAgeBioS: Enhanced Biometric System to handle the Effects of Template Aging

2019 ◽  
Vol 9 (1) ◽  
pp. 3669-3677
Keyword(s):  
Error Rate ◽  
Iris Recognition ◽  
Rejection Rate ◽  
Recognition System ◽  
Biometric System ◽  
Security Systems ◽  
False Rejection Rate ◽  
Biometric Systems ◽  
Template Aging ◽  
False Rejection

Biometric Systems are well-known security systems that can be used anywhere for authentication, authorization or any kind of security verifications. In biometric systems, the samples are trained first and then it can be used for testing in long runs. Many recent researches have shown that a biometric system may fail or get compromised because of the aging of the biometric templates. The fact that temporal duration affects the performance of the biometric system has shattered the belief that iris does not change over lifetime. This is also possible in the case of iris. So, the main focus of this work is to analyze the effect of aging and also to propose a new system that can deal with template aging. We have proposed a new iris recognition system with an image enhancement mechanism and different feature extraction mechanisms. In this work, three different features are extracted, which are then fused to be used as one. The full system is trained on a dataset of 2500 samples for the year 2008 and testing is done in three different phases (i) No-Lapse, (ii) 1-Year Lapse and (iii) 2-Year Lapse. A portion of the ND-Iris-Template-Aging dataset [11] is used with a period of three years lapse. Results show that the performance of the hybrid classifier AHyBrK [17] is improved as compared to KNN and ANN and the effect of aging in terms of degraded performance is clear. The performance of this system is measured in terms of False Rejection Rate, Error Rate, and Accuracy. The overall performance of AHyBrK is 51.04% and 52.98% better than KNN and ANN respectively in terms of False Rejection Rate and Error Rate whereas the accuracy of this proposed system is also improved by 5.52% and 6.04% as compared to KNN and ANN respectively. This proposed system also achieved high accuracy for all the test phases.

scholarly journals Technologies to Overcome Spoofing Attack in Facial Recognition

2019 ◽  
Vol 8 (2S4) ◽  
pp. 36-41
Keyword(s):  
Facial Recognition ◽  
Real Life ◽  
Future Research ◽  
Biometric System ◽  
Security Systems ◽  
Research Directions ◽  
Biometric Systems ◽  
Future Research Directions ◽  
Behavioral Attributes ◽  
Compare And Contrast

The identification technologies used nowadays consists of biometrics as an essential component. The basic use of a conventional biometric system is to identify the authenticity of an individual through its physical as well as behavioral attributes, which is considered as one of the most suitable method to secure confidentiality of data. Though the security of these systems is stringent to breach, still it does consists of vulnerabilities due to various reasons. One of the major threats the current biometric system possess are the spoofing attacks. Spoofing attacks are difficult to conquer due to the fact that a person tries to masquerade as others in order to gain unauthorized access to the security systems. This is one of the biggest problem concerning the integrity of the biometric system. The study of spoofing attacks has gained interest of various researchers in the field of computer science, still there are aspects which needs greater attention in order to achieve a plausible solution. The study is based on the current biometric systems in order to compare and contrast the existing technology used in facial recognition. A detailed review of the existing anti – spoofing methods will be taken into account to discuss the future research directions. Thus, the work will focus on threats to the current security systems, with an aim to analyse the possible countermeasures, and its applications in real life scenarios.

scholarly journals The Effects of Facial Expressions on Face Biometric System’s Reliability

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 485
Author(s):  
Hind A. Alrubaish ◽  
Rachid Zagrouba
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Rejection Rate ◽  
Facial Features ◽  
Biometric System ◽  
Acceptance Threshold ◽  
Face Shape ◽  
Biometric Systems ◽  
The Face ◽  
Face Biometric

The human mood has a temporary effect on the face shape due to the movement of its muscles. Happiness, sadness, fear, anger, and other emotional conditions may affect the face biometric system’s reliability. Most of the current studies on facial expressions are concerned about the accuracy of classifying the subjects based on their expressions. This study investigated the effect of facial expressions on the reliability of a face biometric system to find out which facial expression puts the biometric system at greater risk. Moreover, it identified a set of facial features that have the lowest facial deformation caused by facial expressions to be generalized during the recognition process, regardless of which facial expression is presented. In order to achieve the goal of this study, an analysis of 22 facial features between the normal face and six universal facial expressions is obtained. The results show that the face biometric systems are affected by facial expressions where the disgust expression achieved the most dissimilar score, while the sad expression achieved the lowest dissimilar score. Additionally, the study identified the five and top ten facial features that have the lowest facial deformations on the face shape in all facial expressions. Besides that, the relativity score showed less variances between the sample using the top facial features. The obtained results of this study minimized the false rejection rate in the face biometric system and subsequently the ability to raise the system’s acceptance threshold to maximize the intrusion detection rate without affecting the user convenience.

Multimodal biometric system using deep learning based on face and finger vein fusion

2021 ◽  
pp. 1-13
Author(s):  
Shikhar Tyagi ◽  
Bhavya Chawla ◽  
Rupav Jain ◽  
Smriti Srivastava
Keyword(s):  
High Performance ◽  
Recognition Accuracy ◽  
Error Rates ◽  
Facial Features ◽  
Biometric System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Biometric Systems ◽  
Overall Performance ◽  
Recognition Systems

Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates.

scholarly journals Biometric Systems De-Identification: Current Advancements and Future Directions

2021 ◽  
Vol 1 (3) ◽  
pp. 470-495
Author(s):  
Md Shopon ◽  
Sanjida Nasreen Tumpa ◽  
Yajurv Bhatia ◽  
K. N. Pavan Kumar ◽  
Marina L. Gavrilova
Keyword(s):  
Information Security ◽  
System Development ◽  
User Profile ◽  
Information Privacy ◽  
Biometric System ◽  
Comprehensive Overview ◽  
Future Directions ◽  
Behavioral Biometrics ◽  
Open Questions ◽  
Biometric Systems

Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.

AMELOGLYPHICS-AN UPDATED REVIEW

2021 ◽  
pp. 44-46
Author(s):  
Linda Christabel. S ◽  
Merrylda Claribel. S ◽  
Sushmitha. M ◽  
Mohammed Haroon. A. L ◽  
Karpagam. S ◽  
...  
Keyword(s):  
Soft Tissues ◽  
Forensic Odontology ◽  
Vital Role ◽  
Biometric System ◽  
Hard Tissue ◽  
Gas Leakage ◽  
Biometric Systems ◽  
Stored Information ◽  
Mass Disasters ◽  
Modern Era

In this modern era equipped with technologies, the crime rates are increasing exponentially. This requires newer methodologies to identify a person who is a victim as well as the perpetruator. Automated biometric systems helps in identifying the individuals by the stored information in the database which are unique for each individual. Some of the important methods are ngerprint biometrics and iris scanning.As these methods involves soft tissues they cant be relied upon during mass disasters like burn accidents and gas leakage accidents. Hence, a biometric system using the hard tissue is required for better identication of the individuals. Thus, Ameloglyphics is introduced to aid in identication of individuals died during mass disasters and it plays a vital role in forensic odontology. This review highlights this technology in detail.

Biometric Security

2015 ◽  
pp. 199-217
Author(s):  
Muzhir Shaban Al-Ani
Keyword(s):  
Biological Sciences ◽  
Data Analysis ◽  
Personal Identification ◽  
Biometric System ◽  
Mathematical Methods ◽  
Security Systems ◽  
Biometric Security ◽  
The Future ◽  
Behavioral Parameters ◽  
Biometric Features

The terms biometrics and biometry have been used to refer to the field of development of statistical and mathematical methods applicable to data analysis problems in the biological sciences. Recently biometrics refers to technologies and applications applied for personal identification using physical and behavioral parameters. Biometric security systems ensuring that only the authorized persons are permitted to access a certain data, because it is difficult to copy the biometric features pattern for a specific person. Biometrics is playing an important role in applications that are centric on identification, verification and classification. This chapter focuses on biometric security in their types, specifications, technologies and algorithms. Some algorithms of biometric security are also included in this chapter. Finally latest and future aspects of biometric system and merging technologies are also mentioned, including more details of system structures and specifications and what constitution will shape biometric security of in the future.

Visual Attention for Behavioral Biometric Systems

2010 ◽  
pp. 290-316 ◽  
Author(s):  
Concetto Spampinato
Keyword(s):  
Computational Models ◽  
Careful Analysis ◽  
Recognition System ◽  
Biometric System ◽  
The Third ◽  
Morphological Aspects ◽  
The People ◽  
Face Recognition System ◽  
Biometric Systems ◽  
State Of Art

The chapter is so articulated: the first section will tackle the state of art of the attention theory, with the third paragraph related to the computational models that implement the attention theories, with a particular focus on the model that is the basis for the proposed biometric systems. Such an algorithm will be used for describing the first biometric system. The following section will tackle the people recognition algorithms carried out by evaluating the FOAs distribution. In detail, two different systems are proposed: 1) a face recognition system that takes into account both the behavioral and morphological aspects, and 2) a pure behavioral biometric system that recognizes people according to their actions evaluated by a careful analysis of the extracted FOAs.

Overview

2011 ◽  
pp. 1-23
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Pattern Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Biometric System ◽  
Biometric Recognition ◽  
Biometric Data ◽  
Pattern Recognition System ◽  
Biometric Systems ◽  
History Of

A biometric system can be regarded as a pattern recognition system. In this chapter, we discuss two advanced pattern recognition technologies for biometric recognition, biometric data discrimination and multi-biometrics, to enhance the recognition performance of biometric systems. In Section 1.1, we discuss the necessity, importance, and applications of biometric recognition technology. A brief introduction of main biometric recognition technologies are presented in Section 1.2. In Section 1.3, we describe two advanced biometric recognition technologies, biometric data discrimination and multi-biometric technologies. Section 1.4 outlines the history of related work and highlights the content of each chapter of this book.

A survey of liveness detection methods for face biometric systems

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 346-356 ◽  
Author(s):  
Yang Xin ◽  
Yi Liu ◽  
Zhi Liu ◽  
Xuemei Zhu ◽  
Lingshuang Kong ◽  
...  
Keyword(s):  
Face Recognition ◽  
Future Development ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Detection Methods ◽  
Content Type ◽  
Liveness Detection ◽  
Biometric Systems ◽  
The Future ◽  
Face Biometric

Purpose Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches. Design/methodology/approach This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures. Originality/value This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.

Sign in / Sign up

Export Citation Format

Share Document