scholarly journals Masquerade Attack Analysis for Secured Face Biometric System

2021 ◽  
Vol 10 (2) ◽  
pp. 225-232
Author(s):  
Sanjeeva Kumar M. Hatture ◽  
◽  
Shweta Policepatil ◽  
Keyword(s):  
Hybrid Approach ◽  
Personal Identification ◽  
Biometric System ◽  
Biometric Data ◽  
Left Turn ◽  
Liveness Detection ◽  
Identity Based ◽  
Recognition Systems ◽  
Biometric Trait ◽  
Face Biometric

Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade attack. The masquerade attack is performed using authorized users’ artifact biometric data that may be artifact facial masks, photo or iris photo or any latex finger. This type of attack in Liveness detection has become counter problem in the today's world. To prevent such spoofing attack, we proposed Liveness detection of face by considering the countermeasures and texture analysis of face and also a hybrid approach which combine both passive and active liveness detection is used. Our proposed approach achieves accuracy of 99.33 percentage for face anti-spoofing detection. Also we performed active face spoofing by providing several task (turn face left, turn face right, blink eye, etc) that performed by user on live camera for liveness detection.

Facial recognition technologies and the new physiognomic era

2020 ◽  
Vol 43 (2) ◽  
pp. 45-56
Author(s):  
Abigail Nieves Delgado
Keyword(s):  
Facial Recognition ◽  
Personal Identification ◽  
Digital Photographs ◽  
Different Types ◽  
Recognition Systems ◽  
The Way

The current overproduction of images of faces in digital photographs and videos, and the widespread use of facial recognition technologies have important effects on the way we understand ourselves and others. This is because facial recognition technologies create new circulation pathways of images that transform portraits and photographs into material for potential personal identification. In other words, different types of images of faces become available to the scrutiny of facial recognition technologies. In these new circulation pathways, images are continually shared between many different actors who use (or abuse) them for different purposes. Besides this distribution of images, the categorization practices involved in the development and use of facial recognition systems reinvigorate physiognomic assumptions and judgments (e.g., about beauty, race, dangerousness). They constitute the framework through which faces are interpreted. This paper shows that, because of this procedure, facial recognition technologies introduce new and far-reaching »facialization« processes, which reiterate old discriminatory practices.

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.

Naturalistic Male Skateboard User Speed Study

2020 ◽  
Author(s):  
Sean Buczek ◽  
Lauren Eichaker ◽  
Troy Graham ◽  
Thomas Maull
Keyword(s):  
Traffic Safety ◽  
Sport Participation ◽  
User Behavior ◽  
Public Spaces ◽  
Travel Speed ◽  
Personal Identification ◽  
Accident Reconstruction ◽  
Biometric Data ◽  
Useful Life ◽  
Bearing Design

Abstract Skateboards have been used as a means of transportation and extreme sport participation for decades. However, the prevalence of skateboards as a source of transportation is increasing. The laws that permit skateboard users to travel in roadways and in pedestrian walkways can vary by state, city, or county, allowing for a large variance in travel speed and user behavior. The amount of data available for the average speed of skateboard users during travel and trick initiation is limited. This study will preliminarily describe the natural travel and trick initiation speeds of skateboard users. The data that is presented in this study is beneficial to a vast audience including, but not limited to: traffic safety, road and intersection design, accident reconstruction, skateboard design, bearing design and useful life, and wheel design and useful life. This is an observational study of users on public spaces; no personal identification or biometric data was collected.

scholarly journals Biometric Data Mining Applied to On-line Recognition Systems

10.5772/15836 ◽  
2011 ◽  
Author(s):  
Jose Alberto ◽  
Crispin Zavala ◽  
Ocotlan Diaz ◽  
Gennadiy Burlak ◽  
Alberto Ochoa ◽  
...  
Keyword(s):  
Data Mining ◽  
Biometric Data ◽  
On Line ◽  
Recognition Systems

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.

Cancellable Biometrics for On-line Signature Recognition

2011 ◽  
pp. 290-315 ◽  
Author(s):  
Emanuele Maiorana ◽  
Patrizio Campisi ◽  
Alessandro Neri
Keyword(s):  
Recognition System ◽  
Design Of Algorithms ◽  
Biometric Data ◽  
The Public ◽  
Protection Scheme ◽  
Protection Method ◽  
Signature Database ◽  
On Line ◽  
Signature Recognition ◽  
Recognition Systems

With the widespread diffusion of biometrics-based recognition systems, there is an increasing awareness of the risks associated with the use of biometric data. Significant efforts are therefore being dedicated to the design of algorithms and architectures able to secure the biometric characteristics, and to guarantee the necessary privacy to their owners. In this work we discuss a protected on-line signature-based biometric recognition system, where the considered biometrics are secured by applying a set of non-invertible transformations, thus generating modified templates from which retrieving the original information is computationally as hard as random guessing it. The advantages of using a protection method based on non-invertible transforms are exploited by presenting three different strategies for the matching of the transformed templates, and by proposing a multi-biometrics approach based on score-level fusion to improve the performances of the considered system. The reported experimental results, evaluated on the public MCYT signature database, show that the achievable recognition rates are only slightly affected by the proposed protection scheme, which is able to guarantee the desired security and renewability for the considered biometrics.

scholarly journals Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning

2020 ◽  
Vol 10 (2) ◽  
pp. 18-42
Author(s):  
Norah Abdullah Al-johani ◽  
Lamiaa A. Elrefaei
Keyword(s):  
Deep Learning ◽  
Support Vector ◽  
Biometric System ◽  
Score Level Fusion ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Recognition Systems ◽  
Dorsal Hand Vein ◽  
Level Fusion ◽  
Very High

Advancements in biometrics have attained relatively high recognition rates. However, the need for a biometric system that is reliable, robust, and convenient remains. Systems that use palmprints (PP) for verification have a number of benefits including stable line features, reduced distortion and simple self-positioning. Dorsal hand veins (DHVs) are distinctive for every person, such that even identical twins have different DHVs. DHVs appear to maintain stability over time. In the past, different features algorithms were used to implement palmprint (PP) and dorsal hand vein (DHV) systems. Previous systems relied on handcrafted algorithms. The advancements of deep learning (DL) in the features learned by the convolutional neural network (CNN) has led to its application in PP and DHV recognition systems. In this article, a multimodal biometric system based on PP and DHV using (VGG16, VGG19 and AlexNet) CNN models is proposed. The proposed system is uses two approaches: feature level fusion (FLF) and Score level fusion (SLF). In the first approach, the features from PP and DHV are extracted with CNN models. These extracted features are then fused using serial or parallel fusion and used to train error-correcting output codes (ECOC) with a support vector machine (SVM) for classification. In the second approach, the fusion at score level is done with sum, max, and product methods by applying two strategies: Transfer learning that uses CNN models for features extraction and classification for PP and DHV, then score level fusion. For the second strategy, features are extracted with CNN models for PP and DHV and used to train ECOC with SVM for classification, then score level fusion. The system was tested using two DHV databases and one PP database. The multimodal system is tested two times by repeating PP database for each DHV database. The system achieved very high accuracy rate.

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