scholarly journals Person Verification Based on Multimodal Biometric Recognition

2021 ◽  
Vol 30 (1) ◽  
pp. 161-183
Author(s):  
Annie Anak Joseph ◽  
Alex Ng Ho Lian ◽  
Kuryati Kipli ◽  
Kho Lee Chin ◽  
Dayang Azra Awang Mat ◽  
...  
Keyword(s):  
Promising Result ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Biometric System ◽  
Score Level Fusion ◽  
Person Recognition ◽  
Biometric Systems ◽  
Verification Process ◽  
Multimodal Biometric Recognition ◽  
Person Verification

Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system.

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 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.

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.

scholarly journals Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies

2020 ◽  
Vol 37 (6) ◽  
pp. 889-897
Author(s):  
Abderrahmane Herbadji ◽  
Noubeil Guermat ◽  
Lahcene Ziet ◽  
Zahid Akhtar ◽  
Mohamed Cheniti ◽  
...  
Keyword(s):  
Image Features ◽  
Human Hand ◽  
Biometric System ◽  
Person Identification ◽  
Score Level Fusion ◽  
Triangular Norms ◽  
Person Recognition ◽  
Local Ternary Patterns ◽  
Hand Biometrics ◽  
Fingerprint Database

Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to higher person recognition accuracy; besides maintaining hygienic and pandemic concerns. Towards this aim, in this paper, an effective multi-biometric scheme for person authentication based on contactless fingerprint and palmprint selfies has been proposed. In this study, for simplicity and efficiency, three local descriptors, i.e., local phase quantization (LPQ), local Ternary patterns (LTP), and binarized statistical image features (BSIF), have been employed to extract salient features from contactless fingerprint and palmprint selfies. The score level fusion based multi-biometric system developed in this work combines the matching scores using two different fusion techniques, i.e., transformation based-rules like triangular norms and classifier based-rules like SVM. Experimental results on two publicly available databases (i.e., PolyU contactless to contact-based fingerprint database and IIT-Delhi touchless palmprint dataset) show that the proposed contactless multi-biometric selfie system can easily outperform uni-biometrics.

The Design of Library Management System Based on Internet of Things and Hand Lines Recognition System

2014 ◽  
Vol 672-674 ◽  
pp. 1985-1990 ◽  
Author(s):  
Wang Fang
Keyword(s):  
Management System ◽  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Library Management ◽  
Material Resources ◽  
Library Management System ◽  
Biometric Systems

For an ordinary individual biometric systems and technology, such as fingerprint recognition, palm recognition, face recognition or iris recognition, or late detection from a single object has crippled so that they have the characteristics of unity and limitations, this paper combining fingerprint and hand palm pattern recognition technology, taking into account the complexity of the image pattern and diversity, we propose a dual recognition algorithm, which greatly makes up for lack of a single fingerprint or palm print recognition method. The technology used in library management system than traditional card-borrowed books have higher efficiency and save manpower and material resources. After the experimental statistics, and achieved the desired results, not only improve the recognition efficiency, but also to ensure the accuracy of the recognition performance.

A NEW APPROACH FOR FACE-IRIS MULTIMODAL BIOMETRIC RECOGNITION USING SCORE FUSION

2013 ◽  
Vol 27 (03) ◽  
pp. 1356004 ◽  
Author(s):  
MARYAM ESKANDARI ◽  
ÖNSEN TOYGAR ◽  
HASAN DEMIREL
Keyword(s):  
Recognition Performance ◽  
Recognition System ◽  
Extraction Methods ◽  
Score Level Fusion ◽  
New Approach ◽  
Fusion Methods ◽  
Score Fusion ◽  
Multimodal Biometric Recognition ◽  
Matching Score ◽  
Level Fusion

In this paper, a new approach based on score level fusion is presented to obtain a robust recognition system by concatenating face and iris scores of several standard classifiers. The proposed method concatenates face and iris match scores instead of concatenating features as in feature-level fusion. The features from face and iris are extracted using local and global feature extraction methods such as PCA, subspace LDA, spPCA, mPCA and LBP. Transformation-based score fusion and classifier-based score fusion are then involved in the process to obtain, concatenate and classify the matching scores. Different fusion techniques at matching score level, feature level and decision level are compared with the proposed method to emphasize improvement and effectiveness of the proposed method. In order to validate the proposed scheme, a combined database is formed using ORL and BANCA face databases together with CASIA and UBIRIS iris databases. The results based on recognition performance and ROC analysis demonstrate that the proposed score level fusion achieves a significant improvement over unimodal methods and other multimodal face-iris fusion methods.

PCANet based biometric system with fusion of palmprint and dorsal hand vein

2021 ◽  
pp. 1-9
Author(s):  
Surinder kaur ◽  
Gopal Chaudhary ◽  
Javalkar Dinesh kumar
Keyword(s):  
Recognition System ◽  
Personal Identification ◽  
Identification System ◽  
Biometric System ◽  
Score Level Fusion ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Fusion Methods ◽  
Dorsal Hand Vein ◽  
Level Fusion

Nowadays, Biometric systems are prevalent for personal recognition. But due to pandemic COVID 19, it is difficult to pursue a touch-based biometric system. To encourage a touchless biometric system, a less constrained multimodal personal identification system using palmprint and dorsal hand vein is presented. Hand based Touchless recognition system gives a higher user-friendly system and avoids the spread of coronavirus. A method using Convolution Neural Networks(CNN) to extract discriminative features from the data samples is proposed. A pre-trained function PCANeT is used in the experiments to show the performance of the system in fusion scheme. This method doesn’t require keeping the palm in a specific position or at a certain distance like most other papers. Different patches of ROI are used at two different layers of CNN. Fusion of palmprint and dorsal hand vein is done for final result matching. Both Feature level and score level fusion methods are compared. Results shows the accuracy of upto 98.55% and 98.86% and Equal error rate (EER) of upto 1.22% and 0.93% for score level fusion and feature level fusion, respectively. Our method gives higher accurate results in a less constrained environment.

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.

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