scholarly journals NEW APPROACH TOWARDS EFFICIENT BIOMETRIC SYSTEMS

2011 ◽  
pp. 108-113
Author(s):  
Chander Kant
Keyword(s):  
Recognition System ◽  
Fingerprint Matching ◽  
Biometric System ◽  
Biometric Recognition ◽  
New Approach ◽  
Matching Process ◽  
Biometric Template ◽  
Right Loop ◽  
Biometric Systems ◽  
Special Pattern

Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) Ridge and Furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure. In a traditional biometric recognition system, the biometric template is usually stored on a central server during enrollment. The candidate biometric template captured by the biometric device is sent to the server where the processing and matching steps are performed. The proposed work presents an approach to the processing time during fingerprint matching process in a Biometric System. The proposed work is based upon four major classifications of fingerprint, whorl, arch, left-loop and right-loop and is more efficient as compared with the existing system.

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.

scholarly journals Attacks on Biometric Systems: An Overview

2015 ◽  
Vol 1 (7) ◽  
pp. 283 ◽  
Author(s):  
Rubal Jain ◽  
Chander Kant
Keyword(s):  
Pattern Recognition ◽  
System Architecture ◽  
Recognition System ◽  
Biometric System ◽  
Knowledge Based ◽  
Biometric Template ◽  
Biometric Systems ◽  
Template Protection ◽  
Authentication System ◽  
Biometric Template Protection

Biometrics is a pattern recognition system that refers to the use of different physiological (face, fingerprints, etc.) and behavioral (voice, gait etc.) traits for identification and verification purposes. A biometrics-based personal authentication system has numerous advantages over traditional systems such as token-based (e.g., ID cards) or knowledge-based (e.g., password) but they are at the risk of attacks. This paper presents a literature review of attack system architecture and makes progress towards various attack points in biometric system. These attacks may compromise the template resulting in reducing the security of the system and motivates to study existing biometric template protection techniques to resist these attacks.

scholarly journals Design of Multi-Biometrics for Fake Fingerprint Detection through Body Odor

2019 ◽  
Vol 8 (4) ◽  
pp. 9646-9650
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Body Odor ◽  
Biometric System ◽  
Biometric Recognition ◽  
Odor Recognition ◽  
Biometric Systems ◽  
History Of ◽  
Fingerprint Detection

This work proposes the modernistic multibiometric recognition system for detecting artificial fingerprints and new biometric recognition system to use it in some real-time scenarios. In the recent studies of multi-biometrics, the usage of fingerprint and body odor recognition system stays untouched. This proposed design of a multi-biometric system includes a body odor recognition system along with a fingerprint recognition system that will improve the results in terms of accuracy. The reason behind proposing this model is to detect artificial fingerprints by differentiating the odor of human skin from other materials that are employed in the preparation of artificial fingerprints. This multi-biometric system can be used in forensic labs to identify criminals and to improve the standards of security in authentication of an individual. This multi-biometric system will completely eradicate the use of fake fingerprints and this proposed work will make a remarkable place in real-time applications and the history of multi-biometric systems.

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.

Score-Based Fusion in Multi-Unit Biometric Recognition System

2011 ◽  
Vol 48-49 ◽  
pp. 1010-1013 ◽  
Author(s):  
Yong Li ◽  
Jian Ping Yin ◽  
En Zhu
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Fusion Model ◽  
Biometric Recognition ◽  
Score Level Fusion ◽  
Fusion Rules ◽  
Biometric Systems ◽  
Score Fusion ◽  
Multiple Units ◽  
Level Fusion

The performance of biometric systems can be improved by combining multiple units through score level fusion. In this paper, different fusion rules based on match scores are comparatively studied for multi-unit fingerprint recognition. A novel fusion model for multi-unit system is presented first. Based on this model, we analyze the five common score fusion rules: sum, max, min, median and product. Further, we propose a new method: square. Note that the performance of these strategies can complement each other, we introduce the mixed rule: square-sum. We prove that square-sum rule outperforms square and sum rules. The experimental results show good performance of the proposed methods.

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.

CNN-based multimodal touchless biometric recognition system using Gait and speech

2021 ◽  
pp. 1-10
Author(s):  
Sumit Sarin ◽  
Antriksh Mittal ◽  
Anirudh Chugh ◽  
Smriti Srivastava
Keyword(s):  
Neural Networks ◽  
Weighted Average ◽  
Recognition System ◽  
Biometric Recognition ◽  
Person Identification ◽  
Learning Techniques ◽  
Biometric Systems ◽  
Multimodal Biometric Systems ◽  
Biometric Features ◽  
Identification Model

Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product fusion rules work best for the data used in the experiments.

scholarly journals Hybridization for Classification and Identification of Individuals using Multimodal Biometric Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 2126-2132
Keyword(s):  
Real Life ◽  
Recognition System ◽  
Error Rates ◽  
Gabor Wavelet ◽  
Multimodal Fusion ◽  
Identification System ◽  
Biometric Recognition ◽  
Identification Rate ◽  
Biometric Systems ◽  
Biometric Characteristic

Biometric recognition systems use certain human characteristics such as voice, facial features, fingerprint, iris or hand geometry to identify an individual or verify their identity. These systems have been developed individually for each of these biometric modalities until they achieve remarkable levels of performance. Biometrics is a measure of biological characteristics for the identification or authentication of an individual based on some of its characteristics. Although biometric recognition techniques promise to be very effective, At present, we can not guarantee an excellent identification rate based on a single biometric signature with unimodal biometric systems. Thus the error rates of unimodal biometric systems are relatively high due to all these practical problems, which makes them impractical for the use of critical safety applications. To resolve these problems, a solution is used in the same system in several biometric modalities, called a multimodal biometric system (MBS). MBSs combine different modalities in a unique recognition system. The multimodal fusion allows improving the results obtained by a single biometric characteristic and making the system more robust to noise and interference and more resistant to possible attacks. Fusion may be carried out at the level of signals acquired by the different sensors, of the parameters obtained for each modality, of the scores provided by unimodal experts or of the decision taken by said experts. In the case of fusion, the features obtained from the various biometric methods must be homogenized before the process of fusion is accomplished. This article describes the evolution of a multi-modal biometric identification system depends on 3 biometrics-face, iris & fingerprint. Feature extraction is done using the Gabor Wavelet method and classification is accomplished using the Random Forest classifier. This proposed method is applicable in real-life applications to identify biometric for offices, hospitals, and colleges/universities and so on.

scholarly journals Multi biometric Template Protection using Hybrid Technique

2018 ◽  
Vol 7 (4) ◽  
pp. 2609 ◽  
Author(s):  
G Karthi ◽  
M Ezhilarasan
Keyword(s):  
Recognition System ◽  
Identification System ◽  
Hybrid Technique ◽  
Multiple Traits ◽  
Biometric Recognition ◽  
Authentication Mechanism ◽  
Important Concern ◽  
Biometric Template ◽  
Template Protection ◽  
The Individual

Recently, multi-biometrics system has been the important identification system for providing authentication mechanism. In this pa-per, the multi-biometric recognition system uses multiple traits (face, iris and fingerprint) for authentication. The features are extracted from the traits and feature level fusion technique is applied to the individual features traits to form a fused feature. Protection of these biometrics features against various attacks points is an important concern for authentication process. One such attack is the modification of stored template, which largely affects the performance of biometric recognition system. This paper addresses this concern, by apply-ing template protection algorithm to the biometric features. An improved hybrid template protection algorithm is proposed to protect the biometric template.The experimental results show that the proposed algorithm works better than the existing algorithms available. The proposed algorithm provides better protection to the template. Further, attacks are performed on the proposed system which provide strong resistant against the attacks. 

Sign in / Sign up

Export Citation Format

Share Document