Multimodal biometric systems

Taouche, Chérif; Batouche, Mohamed; Bahaj, Mohamed; Taleb‐Ahmed, Abdelmalik

doi:10.1109/icmcs.2014.6911308

Cited by 13 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(i) Multiple sensors: samples acquired by at least two sensors (ii) Multiple biometrics: analyzing, for instance, palmprint and fingerprint at the same time (iii) Multiple units: integrating information given by two or more fingers (possible when using fingerprints or irises) of a single user (iv) Multiple snapshots: analyzing more than one sample of the same trait taken by the same sensor (v) Multiple classifiers: extracting multiple features and using different classifiers, each for one feature e advantages of multimodal biometrics were presented in [38]. It offers an improvement in the matching accuracy and is less sensitive to imposter attacks and to noise in the sensed data.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Giełczyk

Choraś

Kozik

2019

Mobile Information Systems

View full text Add to dashboard Cite

Palmprint biometrics is a promising modality that enables efficient human identification, also in a mobile scenario. In this paper, a novel approach to feature extraction for palmprint verification is presented. The features are extracted from hand geometry and palmprint texture and fused. The use of a fusion of features facilitates obtaining a higher accuracy and, at the same time, provides more robustness to intrusive factors like illumination, variation, or noise. The major contribution of this paper is the proposition and evaluation of a lightweight verification schema for biometric systems that improves the accuracy without increasing computational complexity which is a necessary requirement in real-life scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Giełczyk

Choraś

Kozik

2019

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Each technology, if used alone to identify or verify someone, has disadvantages, given there may be noisy sensor data, lack of distinctiveness of the biometric trait, unacceptable error rates and spoof attacks (Galbally, Marcel, & Fierrez, 2014;Jain, Nandakumar, & Ross, 2005). While the use of two or more biometric systems simultaneously can overcome some of accuracy and security weakness of using one system (Delac & Grgic, 2004, Taouche, Batouche, Berkane, & Taleb-Ahmed, 2014, few institutions or organizations have developed or deployed multimodal biometric systems.…”

Section: Introductionmentioning

confidence: 99%

Biometric technologies at music festivals: An extended technology acceptance model

Norfolk

O’Regan

2020

Journal of Convention & Event Tourism

View full text Add to dashboard Cite

The purpose of this paper is to gain a better understanding of user acceptance of Biometric Technologies (BT) at outdoor music festivals in the United Kingdom. While research on such technologies, such as facial recognition is limited in the events context, they have already been deployed at music festivals to deal with issues of security, safety and crowd management. Using an extended Technology Acceptance Model (TAM), a self-administered questionnaire was completed by young adults in the United Kingdom who had previously been to a music festival. The study found factors such as privacy, reliability and accuracy did not have a significant impact on user acceptance. Other factors, such as trust, compatibility and convenience were found to have a significant positive impact on perceived ease of use, perceived usefulness and attitude to use. As the findings indicate that accuracy and privacy do not impact BT acceptance, the paper explores how organizers can be transparent and accountable as to their intentions to use BT, so as to justify the usefulness of BT to attendees, artists, regulators and authorities.

show abstract

“…The issue of improving performance can be addressed in a number of ways, but system configurations based on integrating different information sources are a widely adopted means of achieving this [45]. Nevertheless, the often complex analysis required to choose an optimal modality (or many) for an application in the face of such conflicting demands has been a major factor, as has been trying to identify the best classification structures [25].…”

Section: Introductionmentioning

confidence: 99%

An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach

et al. 2020

View full text Add to dashboard Cite

New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique.

show abstract

Multimodal biometric systems

Cited by 13 publications

References 38 publications

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Biometric technologies at music festivals: An extended technology acceptance model

An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach

Contact Info

Product

Resources

About