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 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.

Approach for Protection of Iris Template using Cancelable Biometrics

2019 ◽  
Vol 9 (1) ◽  
pp. 144-152
Author(s):  
Sheetal Chaudhary ◽  
Rajender Nath ◽  
Chander Kant ◽  
Surya Kant
Keyword(s):  
Recognition Performance ◽  
Security And Privacy ◽  
Biometric Authentication ◽  
Cancelable Biometrics ◽  
Biometric Template ◽  
Template Protection ◽  
Biometric Templates

Background and Objective: The most important issue concerning the security of biometric authentication systems is protection of biometric templates. This is because once the biometric template being attacked, it cannot be canceled and reissued. Thus, the intruder could avail the facilities that are meant only for the genuine user just bypassing the enrollment phase. Methods: To protect the biometric templates from attacks, it is advantageous to modify them before storing in the databases through some cancelable and non-invertible transformations. Hence, an approach based on cancelable biometrics is proposed in this paper for providing security and privacy to biometric templates. It uses left iris and right iris as input biometric traits. Different experiments are carried out to authenticate the proposed approach. Results and Conclusion: It satisfies all template protection requirements and expected to show good recognition performance without degrading it.

scholarly journals Accomplishment of New Protocol for Stupendous Security in Cloud Environment

2019 ◽  
Vol 9 (1) ◽  
pp. 2616-2619
Keyword(s):  
Biometric Authentication ◽  
Cloud Environment ◽  
Identity Verification ◽  
Hybrid Clouds ◽  
Biometric Template ◽  
Biometric Encryption ◽  
Authentication And Authorization ◽  
Template Protection ◽  
Encryption Algorithms ◽  
Biometric Template Protection

Cloud is that the rising and thirst area of analysis and advantageous in all the fields. However security is the main disquiet for not espouse cloud for each application. Mainly of the security crisis are connected by authentication with data protection by the respect to cloud security alliance (CSA). The projected New (Biometric encoding and Biometric authentication) protocol can conquer every safety crisis in cloud adjacent. In NEW protocol biometric encryption has been provided for the cloud consumer’s valuable information and identity verification has been utilized in a unique way to scale back the problems associated with authentication and authorization. In NEW protocol Identity verification in cloud environment has been joint by pattern security in coincidence with four entirely dissimilar and influential encryption algorithms for accumulated safety. This protocol improves biometric template protection by the combination of RSA and AES encryption algorithms in proper locations and 3DES, Blowfish has been utilized in information security and solution safety supervision. By implementing such technique can vanish out the un trustiness of adopting cloud, specifically public and hybrid clouds. Since all the users information are hold on in off premise. Adopting this protocol has given nice results when examining with existing work and all the vulnerable places has been considered for improved security.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Online Biological Literature MRI Image Classification Study

2013 ◽  
Vol 694-697 ◽  
pp. 2336-2340
Author(s):  
Yun Feng Yang ◽  
Feng Xian Tang
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Image Features ◽  
Feature Identification ◽  
Identification Method ◽  
Image Library ◽  
Text Information ◽  
Correct Recognition Rate ◽  
Biological Literature ◽  
Mri Image

In order to construct a certain standard structure MRI (Magnetic resonance imaging) image library by extracting and collating unstructured literature data information, an identification method of the image and text information fusion is proposed. The method makes use of PHOW (Pyramid Histogram Of Words) to represent image features, combines with the word frequency characteristics of the embedded icon note (text), and then uses posterior multiplication fusion method to complete the classification and identification of the online biological literature MRI image. The experimental results show that this method has better correct recognition rate and better recognition performance than feature identification method only based on PHOW or text. The study can offer use for reference to construct other structured professional database from online literature.

scholarly journals A Novel Approach of Low-Light Image Denoising for Face Recognition

2014 ◽  
Vol 6 ◽  
pp. 256790
Author(s):  
Yimei Kang ◽  
Wang Pan
Keyword(s):  
Face Recognition ◽  
Image Denoising ◽  
Recognition Rate ◽  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Face Database ◽  
Low Light ◽  
Very Low Frequency ◽  
Light Image

Illumination variation makes automatic face recognition a challenging task, especially in low light environments. A very simple and efficient novel low-light image denoising of low frequency noise (DeLFN) is proposed. The noise frequency distribution of low-light images is presented based on massive experimental results. The low and very low frequency noise are dominant in low light conditions. DeLFN is a three-level image denoising method. The first level denoises mixed noises by histogram equalization (HE) to improve overall contrast. The second level denoises low frequency noise by logarithmic transformation (LOG) to enhance the image detail. The third level denoises residual very low frequency noise by high-pass filtering to recover more features of the true images. The PCA (Principal Component Analysis) recognition method is applied to test recognition rate of the preprocessed face images with DeLFN. DeLFN are compared with several representative illumination preprocessing methods on the Yale Face Database B, the Extended Yale face database B, and the CMU PIE face database, respectively. DeLFN not only outperformed other algorithms in improving visual quality and face recognition rate, but also is simpler and computationally efficient for real time applications.

scholarly journals IDENTIFIKASI IRIS MENGGUNAKAN IMPROVED LEVENBERG-MARQUARDT

2015 ◽  
Vol 1 (1) ◽  
pp. 10
Author(s):  
Rocky Yefrenes Dillak
Keyword(s):  
Hough Transform ◽  
Recognition Rate ◽  
Correct Recognition ◽  
Gray Level ◽  
Maximum Probability ◽  
Circular Hough Transform ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Correct Recognition Rate

Sistem biometrika adalah suatu sistem pengenalan diri menggunakan bagian tubuh atau perilaku manusia seperti sidik jari, telapak tangan, telinga, retina, iris mata, wajah, suhu tubuh, tanda tangan, dll. Iris mata merupakan salah satu biometrika yang sangat stabil, handal, akurat dan merupakan metode autentikasi biometrika tercepat  oleh karena itu merupakan suatu topik penelitian yang sangat diminati oleh banyak peneliti. Penelitian ini bertujuan untuk mengembangkan suatu metode yang dapat digunakan untuk mengidentifikasi secara otomatis seseorang berdasarkan citra iris mata miliknya menggunakan jaringan syaraf tiruan levenberg-marquardt. Penelitian ini menggunakan metode deteksi tepi cany dan circular hough transform untuk segmentasi wilayah iris yang terletak diantara pupil dan sclera serta metode ekstraksi ciri gray level cooccurence matrix (GLCM) yang digunakan untuk ekstraksi ciri. Ciri-ciri tersebut adalah maximum probability, correlation, contrast, energy, homogeneity, dan entropy. Ciri-ciri tersebut kemudian dilatih menggunakan jaringan syaraf tiruan dengan aturan pembelajaran levenberg–marquardt algorithm untuk mengidentifikasi seseorang berdasarkan citra irisnya. Penelitian ini menggunakan 150 data citra iris yang masing-masing terbagi atas 100 data citra iris untuk pelatihan dan 50 data citra iris  untuk pengujian. Berdasarkan hasil pengujian yang dilakukan diperoleh correct recognition rate (CRR) sebesar 99.98%  yang menunjukkan bahwa metode ini dapat digunakan untuk mengidentifikasi secara otomatis seseorang berdasarkan citra iris mata miliknya.

scholarly journals Secure Online Examination with Biometric Authentication and Blockchain-Based Framework

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaoling Zhu ◽  
Chenglong Cao
Keyword(s):  
Access Control ◽  
Biometric Authentication ◽  
Fine Grained ◽  
Fuzzy Vault ◽  
Malicious Behavior ◽  
Online Examination ◽  
E Learning ◽  
Important Means ◽  
Biometric Templates ◽  
Biometric Features

E-learning has been carried out all over the world and then online examinations have become an important means to check learning effect during the outbreak of COVID-19. Participant authenticity, data integrity, and access control are the assurance to online examination. The existing online examination schemes cannot provide the protection of biometric features and fine-grained access control. Particularly, they did not discuss how to resolve some disputes among students, teachers, and a platform in a fair and reasonable way. We propose a novel biometric authentication and blockchain-based online examination scheme. The examination data are encrypted to store in a distributed system, which can be obtained only if the user satisfies decryption policy. And the pieces of evidence are recorded in a blockchain network which is jointly established by some credible institutions. Unlike other examination authentication systems, face templates in our scheme are protected using a fuzzy vault and a cryptographic method. Furthermore, educational administrative department can determine who the real initiator of malicious behavior is when a dispute arises using a dispute determination protocol. Analysis shows that no central authority is required in our scheme; the collusion of multiple users cannot obtain more data; even if the authorities compromise, biometric features of each user will not be leaked. Therefore, in terms of privacy-preserving biometric templates, fine-grained access, and dispute resolution, it is superior to the existing schemes.

scholarly journals Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 354
Author(s):  
Jing Zhou
Keyword(s):  
Feature Extraction ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Recognition Rate ◽  
Nonnegative Matrix ◽  
Facial Feature ◽  
Face Database ◽  
Base Matrix ◽  
Sparse Nonnegative Matrix Factorization ◽  
Pca Method

Weighted nonnegative matrix factorization (WNMF) is a technology for feature extraction, which can extract the feature of face dataset, and then the feature can be recognized by the classifier. To improve the performance of WNMF for feature extraction, a new iteration rule is proposed in this paper. Meanwhile, the base matrix U is sparse based on the threshold, and the new method is named sparse weighted nonnegative matrix factorization (SWNMF). The new iteration rules are based on the smaller iteration steps, thus, the search is more precise, therefore, the recognition rate can be improved. In addition, the sparse method based on the threshold is adopted to update the base matrix U, which can make the extracted feature more sparse and concentrate, and then easier to recognize. The SWNMF method is applied on the ORL and JAFEE datasets, and from the experiment results we can find that the recognition rates are improved extensively based on the new iteration rules proposed in this paper. The recognition rate of new SWNMF method reached 98% for ORL face database and 100% for JAFEE face database, respectively, which are higher than the PCA method, the sparse nonnegative matrix factorization (SNMF) method, the convex non-negative matrix factorization (CNMF) method and multi-layer NMF method.

Combining Watermarking and Hyper-Chaotic Map to Enhance the Security of Stored Biometric Templates

2019 ◽  
Vol 63 (3) ◽  
pp. 479-493 ◽  
Author(s):  
Wadood Abdul ◽  
Ohoud Nafea ◽  
Sanaa Ghouzali
Keyword(s):  
Chaotic Map ◽  
Hybrid Approach ◽  
Discrete Wavelet ◽  
Mean Values ◽  
Face Images ◽  
Biometric Template ◽  
Watermark Embedding ◽  
Biometric Encryption ◽  
The Face ◽  
Template Protection

AbstractThere are a number of issues related to the development of biometric authentication systems, such as privacy breach, consequential security and biometric template storage. Thus, the current paper aims to address these issues through the hybrid approach of watermarking with biometric encryption. A multimodal biometric template protection approach with fusion at score level using fingerprint and face templates is proposed. The proposed approach includes two basic stages, enrollment stage and verification stage. During the enrollment stage, discrete wavelet transform (DWT) is applied on the face images to embed the fingerprint features into different directional sub-bands. Watermark embedding and extraction are done by quantizing the mean values of the wavelet coefficients. Subsequently, the inverse DWT is applied to obtain the watermarked image. Following this, a unique token is assigned for each genuine user and a hyper-chaotic map is used to produce a key stream in order to encrypt a watermarked image using block-cipher. The experimentation results indicate the efficiency of the proposed approach in term of achieving a reasonable error rate of 3.87%.

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