scholarly journals Multibiometric Fusion Authentication in Wireless Multimedia Environment Using Dynamic Bayesian Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Zhendong Wu ◽  
Jiajia Yang ◽  
Jianwu Zhang ◽  
Hengli Yue
Keyword(s):  
Bayesian Method ◽  
Recognition Rate ◽  
Multimodal Fusion ◽  
Fusion Method ◽  
Biometric Authentication ◽  
Wireless Multimedia ◽  
Multimedia Environment ◽  
Multimodal Methods ◽  
Limited Accuracy ◽  
Biometric Fusion

Single biometric method has been widely used in the field of wireless multimedia authentication. However, it is vulnerable to spoofing and limited accuracy. To tackle this challenge, in this paper, we propose a multimodal fusion method for fingerprint and voiceprint by using a dynamic Bayesian method, which takes full advantage of the feature specificity extracted by a single biometrics project and authenticates users at the decision-making level. We demonstrate that this method can be extended to more modal biometric authentication and can achieve flexible accuracy of the authentication. The experiment of the method shows that the recognition rate and stability have been greatly improved, which achieves 4.46% and 5.94%, respectively, compared to the unimodal. Furthermore, it also increases 1.94% when compared with general multimodal methods for the biometric fusion recognition.

scholarly journals Sequential subspace estimator for an efficient multibiometrics authentication and encryption.

2021 ◽  
Author(s):  
Md. Obaidul Malek
Keyword(s):  
Recognition Rate ◽  
Database Management System ◽  
Biometric Authentication ◽  
Face Database ◽  
Biometric Template ◽  
Biometric Encryption ◽  
Cryptographic Keys ◽  
Encryption Method ◽  
Correct Recognition Rate ◽  
Biometric Templates

The principal challenge in biometric authentication is to mitigate the effects of any noise while extracting biometric features for biometric template generation. Most biometric systems are developed under the assumption that the extracted biometrics and the nature of their associated interferences are linear, stationary, and homogeneous. When these assumptions are violated due to nonlinear, nonstationary, and heterogeneous noise, the authentication performance deteriorates. As well, demands for biometric templates are on the rise in the field of information technology, leading to an increase in the vulnerability of stored and dynamic information. Thus, the development of a sophisticated authentication and encryption method is necessary to address these challenges. This dissertation proposes a new Sequential Subspace Estimator (SSE) algorithm for biometric authentication. In the proposed method, a sequential estimator is being designed in the image subspace that addresses challenges arising from nonlinear, nonstationary, and heterogeneous noise. The proposed method includes a subspace technique that overcomes the computational complexity associated with the sequential estimator. In addition, it includes a novel MultiBiometrics encryption algorithm that protects the biometric templates against security, privacy, and unlinkability attacks. Unlike current biometric encryption, this method uses cryptographic keys in conjunction with extracted MultiBiometrics to create cryptographic bonds, called “BioCryptoBond”. To further enhance system security and improve authentication accuracy, the development of a biometric database management system is also being considered. The proposed method is being tested on images from three public databases: the “Put Face Database”, the “Indian Face Database”, and the “CASIA Fingerprint Image Database Version 5.1”. The performance of the proposed solution has been evaluated using the Equal Error Rate (EER) and Correct Recognition Rate (CRR). The experimental results demonstrate the superiority of the proposed method in comparison to its counterparts.

Mine Loadometer License Plate Recognition Based on MFF-DS

2013 ◽  
Vol 712-715 ◽  
pp. 2341-2344 ◽  
Author(s):  
Xiu Cai Guo ◽  
Shi Qian Zhang
Keyword(s):  
Feature Fusion ◽  
Recognition Rate ◽  
Evidence Theory ◽  
Support Vector ◽  
License Plate ◽  
Final Decision ◽  
Fusion Method ◽  
License Plate Recognition ◽  
Single Feature ◽  
Basic Probability

The result of license plate recognition with a single feature is unsatisfactory. A multi-feature fusion method based on D-S evidence theory is proposed to improve results of mine loadometer license plate recognition. Firstly, three kinds of features including contour, projection and trellis-coded are extracted from the vehicle plate character image. Then the Basic Probability Assignment (BPA) is defined to get the credibility of recognition results by using the multi-class Support Vector Machine (SVM) with one-against-one method. Finally, D-S evidence theory is employed to integrate the credibility of evidences for making a final decision. The experimental results show that the multi-feature fusion method has higher recognition rate, fault tolerance and robustness.

scholarly journals Multimodal Fusion Method Based on Self-Attention Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hu Zhu ◽  
Ze Wang ◽  
Yu Shi ◽  
Yingying Hua ◽  
Guoxia Xu ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Data Fusion ◽  
Heterogeneous Data ◽  
Attention Mechanism ◽  
Multimodal Fusion ◽  
Fusion Method ◽  
Data Set ◽  
Multimodal Data ◽  
Public Data ◽  
Multimodal Data Fusion

Multimodal fusion is one of the popular research directions of multimodal research, and it is also an emerging research field of artificial intelligence. Multimodal fusion is aimed at taking advantage of the complementarity of heterogeneous data and providing reliable classification for the model. Multimodal data fusion is to transform data from multiple single-mode representations to a compact multimodal representation. In previous multimodal data fusion studies, most of the research in this field used multimodal representations of tensors. As the input is converted into a tensor, the dimensions and computational complexity increase exponentially. In this paper, we propose a low-rank tensor multimodal fusion method with an attention mechanism, which improves efficiency and reduces computational complexity. We evaluate our model through three multimodal fusion tasks, which are based on a public data set: CMU-MOSI, IEMOCAP, and POM. Our model achieves a good performance while flexibly capturing the global and local connections. Compared with other multimodal fusions represented by tensors, experiments show that our model can achieve better results steadily under a series of attention mechanisms.

Adaptive Fusion of Multi-Biometrics for Human Identification in Video

2015 ◽  
Vol 719-720 ◽  
pp. 1013-1018
Author(s):  
Ying Hui Kong ◽  
Pei Yao Chen
Keyword(s):  
Feature Fusion ◽  
Recognition Performance ◽  
Decision Fusion ◽  
Fusion Method ◽  
Recognition Method ◽  
Fusion Rules ◽  
Discriminating Power ◽  
Adaptive Fusion ◽  
Biometric Fusion ◽  
Fusion Features

The purpose of multiple biometric fusion is to improve the recognition performance by utilizing their complementary. In this paper, the feature fusion recognition method of multi-view face and gait in video is studied, and a adaptive decision fusion method is proposed. The results showed that the adaptive fusion features carry the most discriminating power compared to any individual biometric and other static fusion rules like Max and Sum.

A Fusion Method of Smile and Laugh Expression Classification

2011 ◽  
Vol 58-60 ◽  
pp. 2364-2369
Author(s):  
Jun Chen ◽  
Li Hua Guo ◽  
Yang Bai
Keyword(s):  
Real World ◽  
Classification System ◽  
Recognition Rate ◽  
Features Extraction ◽  
Data Sets ◽  
Fusion Method ◽  
Hybrid Features ◽  
Recognition Algorithms ◽  
Expression Classification

This paper proposed to build a smile expression classification system on data sets of GENKI that can represent real-world environments, and tested its implementation, in which we got the optimal recognition rate up to 86.197%. To deal with the features extraction problems, hybrid features (i.e., Gabor, PHOG, PLBP) are used, using hybrid recognition algorithms (i.e., GentleBoost, SVM) to classify, in this paper. Experiments showed the effectiveness of our methods.

scholarly journals Sequential subspace estimator for an efficient multibiometrics authentication and encryption.

2021 ◽  
Author(s):  
Md. Obaidul Malek
Keyword(s):  
Recognition Rate ◽  
Database Management System ◽  
Biometric Authentication ◽  
Face Database ◽  
Biometric Template ◽  
Biometric Encryption ◽  
Cryptographic Keys ◽  
Encryption Method ◽  
Correct Recognition Rate ◽  
Biometric Templates

The principal challenge in biometric authentication is to mitigate the effects of any noise while extracting biometric features for biometric template generation. Most biometric systems are developed under the assumption that the extracted biometrics and the nature of their associated interferences are linear, stationary, and homogeneous. When these assumptions are violated due to nonlinear, nonstationary, and heterogeneous noise, the authentication performance deteriorates. As well, demands for biometric templates are on the rise in the field of information technology, leading to an increase in the vulnerability of stored and dynamic information. Thus, the development of a sophisticated authentication and encryption method is necessary to address these challenges. This dissertation proposes a new Sequential Subspace Estimator (SSE) algorithm for biometric authentication. In the proposed method, a sequential estimator is being designed in the image subspace that addresses challenges arising from nonlinear, nonstationary, and heterogeneous noise. The proposed method includes a subspace technique that overcomes the computational complexity associated with the sequential estimator. In addition, it includes a novel MultiBiometrics encryption algorithm that protects the biometric templates against security, privacy, and unlinkability attacks. Unlike current biometric encryption, this method uses cryptographic keys in conjunction with extracted MultiBiometrics to create cryptographic bonds, called “BioCryptoBond”. To further enhance system security and improve authentication accuracy, the development of a biometric database management system is also being considered. The proposed method is being tested on images from three public databases: the “Put Face Database”, the “Indian Face Database”, and the “CASIA Fingerprint Image Database Version 5.1”. The performance of the proposed solution has been evaluated using the Equal Error Rate (EER) and Correct Recognition Rate (CRR). The experimental results demonstrate the superiority of the proposed method in comparison to its counterparts.

scholarly journals New Aadhaar with Multimodal Biometric Model-Based E-Voting System with Dynamic Hybrid IANFIS-PSO

2020 ◽  
Vol 9 (4) ◽  
pp. 1416-1421
Keyword(s):  
Recognition Rate ◽  
Biometric Authentication ◽  
Voting Systems ◽  
Indian Government ◽  
High Quality ◽  
Critical Function ◽  
Biometric Systems ◽  
Voting System ◽  
Dynamic Hybrid ◽  
Voter Id

This work deals with the E-voting system with a biometric concept that will make the voting system smart, secure and easy to vote which can be linked with Aadhaar card. While the process of doing the Aadhaar enrolment process Authorities gathered information of fingerprints and iris of every character and this whole fact of every person persists in the Indian government database. However these two biometric is not enough for the voter authentication process, besides improving the recognition rate, combining multimodal biometric modalities might be more appropriate for E-voting applications. If the Indian Government link this database to the voter ID present in these days vote casting gadget, then all of us can easily forge their votes the use of multimodal biometric authentication. With this motivation, the new Aadhaar with multimodal biometric-based E-voting systems (AMBEVS) system is designed in this work and it allows users to be confirmed using either modality. Here the validation of the voters is verified with the use of Dynamic Hybrid ANFIS-PSO. A critical function and objective of the proposed gadget are to decorate the photograph high-quality and low diploma of complexity for the security of multimodal biometric reputation frameworks. The experimental results show that the proposed AMBEVS are more robust, reliable and accurate as compared to the unimodal based biometric systems.

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