Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique

2013 ◽  
Vol 5 (3) ◽  
pp. 22-32
Author(s):  
Dhiman Karmakar ◽  
Madhura Datta ◽  
C.A. Murthy
Keyword(s):  
Selection Method ◽  
Estimation Technique ◽  
Minimal Spanning Tree ◽  
Biometric Recognition ◽  
Multimodal Biometrics ◽  
Iris Biometrics ◽  
Set Estimation ◽  
Biometric Systems ◽  
Multimodal Biometric Recognition ◽  
Better Than

Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique.

GENETIC ALGORITHM BASED FEATURE SELECTION LEVEL FUSION USING FINGERPRINT AND IRIS BIOMETRICS

2008 ◽  
Vol 22 (03) ◽  
pp. 585-600 ◽  
Author(s):  
A. ALPASLAN ALTUN ◽  
H. ERDINC KOCER ◽  
NOVRUZ ALLAHVERDI
Keyword(s):  
Feature Selection ◽  
Degrees Of Freedom ◽  
Recognition System ◽  
Test Results ◽  
Biometric Recognition ◽  
Training Time ◽  
Iris Biometrics ◽  
Multimodal Biometric Recognition ◽  
Selection Of ◽  
Combined Data

An accuracy level of unimodal biometric recognition system is not very high because of noisy data, limited degrees of freedom, spoof attacks etc. problems. A multimodal biometric system which uses two or more biometric traits of an individual can overcome such problems. We propose a multimodal biometric recognition system that fuses the fingerprint and iris features at the feature extraction level. A feed-forward artificial neural networks (ANNs) model is used for recognition of a person. There is a need to make the training time shorter, so the feature selection level should be performed. A genetic algorithms (GAs) approach is used for feature selection of a combined data. As an experiment, the database of 60 users, 10 fingerprint images and 10 iris images taken from each person, is used. The test results are presented in the last stage of this research.

Review and Applications of Multimodal Biometrics for Secured Systems

2017 ◽  
pp. 251-266
Author(s):  
Chitra Anil Dhawale
Keyword(s):  
Access Control ◽  
Biometric Identification ◽  
Control Methods ◽  
Biometric Recognition ◽  
Multimodal Biometrics ◽  
Multimodal Systems ◽  
Average Accuracy ◽  
Biometric Systems ◽  
Electronic Access ◽  
Multimodal Biometric Systems

Biometric Systems provide improved security over traditional electronic access control methods such as RFID tags, electronic keypads and some mechanical locks. The user's authorized card or password pin can be lost or stolen. In order for the biometrics to be ultra-secure and to provide more-than-average accuracy, more than one form of biometric identification is required. Hence the need arises for the use of multimodal biometrics. This uses a combination of different biometric recognition technologies. This chapter begins with the basic idea of Biometrics, Biometrics System with its components, Working and proceeds with the need of Multimodal Biometrics with the emphasis on review of various multimodal systems based on fusion ways and fusion level of various features. The last section of this chapter describes various multimodal Biometric Systems.

scholarly journals Multimodal Biometric Recognition: Fusion of Modified Adaptive Bilinear Interpolation Data Samples of Face and Signature using Local Binary Pattern Features

2020 ◽  
Vol 9 (3) ◽  
pp. 3111-3120
Keyword(s):  
Nearest Neighbor ◽  
Ensemble Methods ◽  
K Nearest Neighbor ◽  
Bilinear Interpolation ◽  
Biometric Recognition ◽  
Multimodal Biometrics ◽  
Quality Performance ◽  
Computer Laboratory ◽  
Current Scenario ◽  
Multimodal Biometric Recognition

Biometric based authentication systems use particular person characteristics which might be based on either behavior like voice, signature etc. or body structure like face, iris, palm print, fingerprint, etc. The performance of any unimodal biometric arrangement is depending on elements like surroundings, atmosphere, and sensor precision. Also, there are numerous trait unique demanding situations which include pose, expression, growing old and so forth for face reputation, occlusion and acquisition related problems for iris and terrible high-quality and social popularity related troubles for fingerprint. Hence, fusion of more than one biometric samples, traits or algorithms to achieve quality performance is another way to reap the better overall performance. In current scenario many researchers concentrating on Multimodal Biometrics with new fusion techniques ideas. We propose a new method of feature level fusion which uses Modified Adaptive Bilinear Interpolation (MABI) method to increase the resolution of data sample, which gives better features for fusion which gives more accurate results. In this work, experiment is done on AT&T face Cambridge University Computer Laboratory and MCYT signature Biometric Recognition Group datasets with combination of both unimodal and multimodal traits. K Nearest Neighbor (KNN) and Ensemble methods are used for classification. The proposed biometric system can be used in biometric surveillance, biometric screening for secured places, forensic applications etc.

scholarly journals Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature

2021 ◽  
Vol 3 (2) ◽  
pp. 131-143
Author(s):  
Vijayakumar T.
Keyword(s):  
Feature Fusion ◽  
Developed Countries ◽  
Biometric Recognition ◽  
Accurate Identification ◽  
Multiple Features ◽  
Research Attention ◽  
Research Article ◽  
Biometric Systems ◽  
Palm Print ◽  
Multimodal Biometric Recognition

Biometric identification technology is widely utilized in our everyday lives as a result of the rising need for information security and safety laws throughout the world. In this aspect, multimodal biometric recognition (MBR) has gained significant research attention due to its ability to overcome several important constraints in unimodal biometric systems. Henceforth, this research article utilizes multiple features such as an iris, face, finger vein, and palm print for obtaining the highest accuracy to identify the exact person. The utilization of multiple features from the person improves the accuracy of biometric system. In many developed countries, palm print features are employed to provide the most accurate identification of an actual individual as fast as possible. The proposed system can be very suitable for the person who dislikes answering many questions for security authentication. Moreover, the proposed system can also be used to minimize the extra questionnaire by achieving a highest accuracy than other existing multimodal biometric systems. Finally, the results are computed and tabulated in this research article.

Estimation Design Using Youla Parametrization With Automotive Applications

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Francis Assadian ◽  
Alex K. Beckerman ◽  
Jose Velazquez Alcantar
Keyword(s):  
Kalman Filtering ◽  
Feedback Stabilization ◽  
Estimation Technique ◽  
Single Input Single Output ◽  
Normal Forces ◽  
Single Output ◽  
Multiple Input ◽  
Single Input ◽  
Established Technique ◽  
Better Than

Youla parametrization is a well-established technique in deriving single-input single-output (SISO) and, to a lesser extent, multiple-input multiple-ouput (MIMO) controllers (Youla, D., Bongiorno, J. J., Jr., and Lu, C., 1974, “Singleloop Feedback-Stabilization of Linear Multivariable Dynamical Plants,” Automatica, 10(2), pp. 159–173). However, the utility of this methodology in estimation design, specifically in the framework of controller output observer (COO) (Ozkan, B., Margolis, D., and Pengov, M., 2008, “The Controller Output Observer: Estimation of Vehicle Tire Cornering and Normal Forces,” ASME J. Dyn. Syst., Meas., Control, 130(6), p. 061002), is not established. The fundamental question to be answered is as follows: is it possible to design a deterministic estimation technique using Youla paramertization with the same robust performance, or better, than well-established stochastic estimation techniques such as Kalman filtering? To prove this point, at this stage, a comparative analysis between Youla parametrization in estimation and Kalman filtering is performed through simulations only. In this paper, we provide an overview of Youla parametrization for both control and estimation design. We develop a deterministic SISO and MIMO Youla estimation technique in the framework of COO, and we investigate the utility of this method for two applications in the automotive domain.

A Multimodal Biometric Recognition of Touched Fingerprint and Finger-Vein

2011 ◽  
Author(s):  
Young Ho Park ◽  
Dat Nguyen Tien ◽  
Hyeon Chang Lee ◽  
Kang Ryoung Park ◽  
Eui Chul Lee ◽  
...  
Keyword(s):  
Biometric Recognition ◽  
Finger Vein ◽  
Multimodal Biometric Recognition

Analytical Review of Biometric Technology Employing Vivid Modalities

2021 ◽  
pp. 2250004
Author(s):  
Vandana ◽  
Navdeep Kaur
Keyword(s):  
Security And Privacy ◽  
Future Research ◽  
Multimodal Biometrics ◽  
Biometric Technology ◽  
Practical Applications ◽  
Biometric Systems ◽  
Potential Applications ◽  
Analytical Review ◽  
Comprehensive Survey ◽  
Technology Market

The digitalization has been challenged with the security and privacy aspects in each and every field. In addition to numerous authentication methods, biometrics has been popularized as it relies on one’s individual behavioral and physical characters. In this context, numerous unimodal and multimodal biometrics have been proposed and tested in the last decade. In this paper, authors have presented a comprehensive survey of the existing biometric systems while highlighting their respective challenges, advantage and limitations. The paper also discusses the present biometric technology market value, its scope, and practical applications in vivid sectors. The goal of this review is to offer a compact outline of various advances in biometrics technology with potential applications using unimodal and multimodal bioinformatics are discussed that would prove to offer a base for any biometric-based future research.

scholarly journals Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5523 ◽  
Author(s):  
Nada Alay ◽  
Heyam H. Al-Baity
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Recognition Performance ◽  
Identification System ◽  
Biometric Recognition ◽  
Multimodal Biometrics ◽  
Score Level Fusion ◽  
Softmax Classifier ◽  
Finger Vein ◽  
Level Fusion

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

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.

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