Biometric Cryptosystem Involving Two Traits And Palm Vein As Key

Balaji, Prasanalakshmi; Kannammal, A.; Gomathi, B.; Deepa, K.; Sridevi, R.

doi:10.1016/j.proeng.2012.01.865

Cited by 21 publications

(13 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fitness is mathematically given in equation (7). (7) ) ( FP FN TP TN TP TN Accuracy      (8) After fitness calculation, the obtained solution is given for the next step i.e. updation solution.…”

Section: Selecting Optimal Features Using Rider Optimization Algorithmentioning

confidence: 99%

“…Two major uses of biometric frameworks are Authentication or Verification and Identification, depending on the selected biometric attribute. This enlisted biometric value could be an individual's physiological or social attribute that meets the needs of comprehensiveness, individuality, eternal quality and collectability [8]. The ear is an interesting human element.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

2019

IJRTE

View full text Add to dashboard Cite

Biometrics provides greater security and usability than conventional personal authentication methods. Fingerprints, facial identification systems and voice recognition systems are the features that biometric systems can use. To improve biometric authentication, the proposed method considered that the input image is iris and fingerprint; at first, pre-processing is performed through histogram equalization for all image inputs to enhance the image quality. Then the extraction process of the feature will be performed. The suggested method uses modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features next to the extracted features to be combined for feature extraction. Then the optimum function is found with the Rider Optimization Algorithm (ROA) for all MLBP, GLCM and DWT. Eventually, the approach suggested is accepted. Deep Neural Network (DNN) performs the proposed authentication process. A DNN is a multilayered artificial neural network between the layers of input and output. The DNN finds the right mathematical manipulation to turn the input into the output, whether it is an acknowledged image or not. Suggested process quality is measured in terms of reliability recognition. In the MATLAB platform, the suggested approach is implemented.

show abstract

Section: Selecting Optimal Features Using Rider Optimization Algorithmentioning

confidence: 99%

mentioning

confidence: 99%

Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…Also a better fusion scheme can be used which generate a more compact biometric template which stores most of the information in the individual template. Prasanalakshmi et al (2011) used three biometric traits such as face, fingerprint and palm vein to form a multimodal system and palm vein features are used as key for encryption. The features are extracted from all the three modalities.…”

Section: Multimodal Biometric Cryptosystemmentioning

confidence: 99%

Multimodal biometric cryptosystem based on fusion of wavelet and curvelet features in robust security application

Inamdar¹,

Dandawate²

2016

IJBM

View full text Add to dashboard Cite

The authentication system used at the automated teller machines (ATM) is a unique personal identification number (PIN). This PIN can be easily tapped and misused. In this paper we propose a method in which the PIN is replaced by the biometrics of the individual to have a more secure transaction. A complete hardware system is designed to capture the biometric traits such as face, fingerprint and palm vein. The captured images are pre-processed and then features are extracted which are then fused at feature level. Cryptography is applied on the fused feature vector. Matching is performed using Euclidean distance at the server end. Palm vein is particularly chosen as a biometric trait along with widely used face and fingerprint because it is unique and is impossible to forge the vein pattern of an individual. Curvelet and Wavelet Transforms are used for the feature extraction. Experimental results indicate a good level of security and recognition rate of 91% and 89% is achieved on our own generated database. The results are promising when compared with other existing similar techniques.Reference to this paper should be made as follows: Inamdar, S.R. and Dandawate, Y.H. (2016) 'Multimodal biometric cryptosystem based on fusion of wavelet and curvelet features in robust security application', Int. He has 23 years of teaching experience and has published more than 55 papers 34 S.R. Inamdar and Y.H. Dandawate in reputed international and national conferences/referred journals. His areas of interest are in signal and image processing, embedded systems and soft computing. He is a reviewer and editorial board member of reputed journals in India and abroad. He is also working on several research projects. He is a senior member of IEEE and Fellow member of IETE, India.

show abstract

“…Security gateway logs the details of the failed authentication in the auditing log, creating an audit trail for security administrators to investigate later. On a successful user and system authentication, the Biobex server displays its input page to gather multimodal biometric information as stored in the space specified token [5] and on final authentication, Security Gateway then determines all authorization policies that apply to the user when accessing the cloud application. The user's ID, group, and role within the enterprise are considered when evaluating the applicable policies.…”

Section: A Detailed Look At the Authentication Processmentioning

confidence: 99%

Secure Credential Federation for Hybrid Cloud Environment with SAML Enabled Multifactor Authentication using Biometrics

Balaji¹,

Kannammal²

2012

IJCA

Self Cite

View full text Add to dashboard Cite

Cloud computing is introducing many huge changes to people's lifestyle and working pattern recently for its multitudinous benefits. However, the security of cloud computing is always the focus of numerous potential cloud customers, and a big barrier for its widespread applications.Companies have increasingly turned to application service providers (ASPs) or Software as a Service (SaaS) vendors to offer specialized web-based services that will cut costs and provide specific and focused applications to users. The complexity of designing, installing, configuring, deploying, and supporting the system with internal resources can be eliminated with this type of methodology, providing great benefit to organizations. However, these models can present an authentication problem for corporations with a large number of external service providers. This paper describes the implementation of Security Assertion Markup Language (SAML) and its capabilities to provide secure single sign-on (SSO) solutions for externally hosted applications, including security measures for federated identity management systems using multifactor authentication ,which also includes Biometric identification. General TermsComputer networks and security.

show abstract

Biometric Cryptosystem Involving Two Traits And Palm Vein As Key

Cited by 21 publications

References 9 publications

Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

Multimodal biometric cryptosystem based on fusion of wavelet and curvelet features in robust security application

Secure Credential Federation for Hybrid Cloud Environment with SAML Enabled Multifactor Authentication using Biometrics

Contact Info

Product

Resources

About