scholarly journals To study of various security attacks against Biometric template in a generic Biometric Recognition System

2017 ◽  
Author(s):  
Manish Kumar ◽  
Kunwar Singh Vaisla
Keyword(s):  
Recognition System ◽  
Security Attacks ◽  
Biometric Recognition ◽  
Biometric Template

scholarly journals NEW APPROACH TOWARDS EFFICIENT BIOMETRIC SYSTEMS

2011 ◽  
pp. 108-113
Author(s):  
Chander Kant
Keyword(s):  
Recognition System ◽  
Fingerprint Matching ◽  
Biometric System ◽  
Biometric Recognition ◽  
New Approach ◽  
Matching Process ◽  
Biometric Template ◽  
Right Loop ◽  
Biometric Systems ◽  
Special Pattern

Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) Ridge and Furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure. In a traditional biometric recognition system, the biometric template is usually stored on a central server during enrollment. The candidate biometric template captured by the biometric device is sent to the server where the processing and matching steps are performed. The proposed work presents an approach to the processing time during fingerprint matching process in a Biometric System. The proposed work is based upon four major classifications of fingerprint, whorl, arch, left-loop and right-loop and is more efficient as compared with the existing system.

scholarly journals Multi biometric Template Protection using Hybrid Technique

2018 ◽  
Vol 7 (4) ◽  
pp. 2609 ◽  
Author(s):  
G Karthi ◽  
M Ezhilarasan
Keyword(s):  
Recognition System ◽  
Identification System ◽  
Hybrid Technique ◽  
Multiple Traits ◽  
Biometric Recognition ◽  
Authentication Mechanism ◽  
Important Concern ◽  
Biometric Template ◽  
Template Protection ◽  
The Individual

Recently, multi-biometrics system has been the important identification system for providing authentication mechanism. In this pa-per, the multi-biometric recognition system uses multiple traits (face, iris and fingerprint) for authentication. The features are extracted from the traits and feature level fusion technique is applied to the individual features traits to form a fused feature. Protection of these biometrics features against various attacks points is an important concern for authentication process. One such attack is the modification of stored template, which largely affects the performance of biometric recognition system. This paper addresses this concern, by apply-ing template protection algorithm to the biometric features. An improved hybrid template protection algorithm is proposed to protect the biometric template.The experimental results show that the proposed algorithm works better than the existing algorithms available. The proposed algorithm provides better protection to the template. Further, attacks are performed on the proposed system which provide strong resistant against the attacks. 

scholarly journals Cryptographic Algorithm based Feature Level Fusion of Fingerprint and Iris in a Multi-Biometric Recognition System

2019 ◽  
Vol 9 (2) ◽  
pp. 2354-2360
Keyword(s):  
Principal Component ◽  
Recognition System ◽  
Kernel Principal Component Analysis ◽  
Dynamic Features ◽  
Biometric Recognition ◽  
Cryptographic Algorithm ◽  
Biometric Template ◽  
Cryptographic Keys ◽  
Occurrence Matrix ◽  
Exploration Area

Biometric encryption is one of the developing exploration area, which is a strategy for merging biometric features with cryptographic keys. Biometric Recognition is based on the anatomical and behavior attributes of the individuals. Multibiometric is the combination of various biometrics like Fingerprint, Iris, and Face, Fingervein etc. Experts are concentrating on the most proficient method to give security to the framework, the template which was produced from the biometric should be ensured. The main objective of this paper is to protect the multi biometric template by creating a protected sketch by deploying bio cryptosystem. Once the biometric template is stolen it turns into a major issue for the security of the framework and furthermore for client protection. In this way, a bio-crypto framework ensures the confidentiality of the information. In this paper bio cryptosystem is proposed to improve the security of multimodal frameworks by producing the biocrypto key from Finger print and iris. Gray level co-occurrence matrix (GLCM) based Haralick features, local binary pattern (LBP), triplet half-band filter bank (THFB) and dynamic features (DF) are extracted from fingerprint and iris. The high dimensionality space of the features are reduced using kernel principal component analysis (KPCA. Finally, the encoding process is matted with biometric key utilizing symmetric RSA (Rivest-Shamir-Adleman) cryptographic algorithm.

Multimodal score level fusion for recognition using face and palmprint

2020 ◽  
pp. 002072092092966
Author(s):  
Milind E Rane ◽  
Umesh S Bhadade
Keyword(s):  
Recognition System ◽  
Acceptance Rate ◽  
Face Database ◽  
Biometric Recognition ◽  
False Acceptance Rate ◽  
Score Level Fusion ◽  
Score Fusion ◽  
Matching Score ◽  
Level Fusion ◽  
Fusion Approach

The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

scholarly journals CAPSULE NETWORK BASED BIOMETRIC RECOGNITION SYSTEM

2019 ◽  
Vol 2019 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Dr. I. Jeena Jacob
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
Noise Removal ◽  
Personal Identification ◽  
Biometric Recognition ◽  
Learning Techniques ◽  
Frame Work ◽  
The Stability

The biometric recognition plays a significant and a unique part in the applications that are based on the personal identification. This is because of the stability, irreplaceability and the uniqueness that is found in the biometric traits of the humans. Currently the deep learning techniques that are capable of strongly generalizing and automatically learning, with the enhanced accuracy is utilized for the biometric recognition to develop an efficient biometric system. But the poor noise removal abilities and the accuracy degradation caused due to the very small disturbances has made the conventional means of the deep learning that utilizes the convolutional neural network incompatible for the biometric recognition. So the capsule neural network replaces the CNN due to its high accuracy in the recognition and the classification, due to its learning capacities and the ability to be trained with the limited number of samples compared to the CNN (convolutional neural network). The frame work put forward in the paper utilizes the capsule network with the fuzzified image enhancement for the retina based biometric recognition as it is a highly secure and reliable basis of person identification as it is layered behind the eye and cannot be counterfeited. The method was tested with the dataset of face 95 database and the CASIA-Iris-Thousand, and was found to be 99% accurate with the error rate convergence of 0.3% to .5%

Sign in / Sign up

Export Citation Format

Share Document