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 Biometric iris templates security based on secret image sharing and chaotic maps

2022 ◽  
Vol 12 (1) ◽  
pp. 339
Author(s):  
Marwa Fadhel Jassim ◽  
Wafaa mohammed Saeed Hamzah ◽  
Abeer Fadhil Shimal
Keyword(s):  
Safety Issue ◽  
Recognition System ◽  
Security And Privacy ◽  
Secret Image Sharing ◽  
Data Set ◽  
Secret Image ◽  
Image Sharing ◽  
Biometric Template ◽  
Behavioural Characteristics ◽  
Template Protection

Biometric technique includes of uniquely identifying person based on their physical or behavioural characteristics. It is mainly used for authentication. Storing the template in the database is not a safe approach, because it can be stolen or be tampered with. Due to its importance the template needs to be protected. To treat this safety issue, the suggested system employed a method for securely storing the iris template in the database which is a merging approach for secret image sharing and hiding to enhance security and protect the privacy by decomposing the template into two independent host (public) iris images. The original template can be reconstructed only when both host images are available. Either host image does not expose the identity of the original biometric image. The security and privacy in biometrics-based authentication system is augmented by storing the data in the form of shadows at separated places instead of whole data at one. The proposed biometric recognition system includes iris segmentation algorithms, feature extraction algorithms, a (2, 2) secret sharing and hiding. The experimental results are conducted on standard colour UBIRIS v1 data set. The results indicate that the biometric template protection methods are capable of offering a solution for vulnerability that threatens the biometric template.

scholarly journals Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Wenwen Li
Keyword(s):  
Recognition Rate ◽  
Principal Component ◽  
Texture Features ◽  
Single Mode ◽  
Recognition System ◽  
Local Features ◽  
Identification System ◽  
Biometric Recognition ◽  
Global Features ◽  
Hand Shape

Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users’ satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.

scholarly journals Hybridization for Classification and Identification of Individuals using Multimodal Biometric Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 2126-2132
Keyword(s):  
Real Life ◽  
Recognition System ◽  
Error Rates ◽  
Gabor Wavelet ◽  
Multimodal Fusion ◽  
Identification System ◽  
Biometric Recognition ◽  
Identification Rate ◽  
Biometric Systems ◽  
Biometric Characteristic

Biometric recognition systems use certain human characteristics such as voice, facial features, fingerprint, iris or hand geometry to identify an individual or verify their identity. These systems have been developed individually for each of these biometric modalities until they achieve remarkable levels of performance. Biometrics is a measure of biological characteristics for the identification or authentication of an individual based on some of its characteristics. Although biometric recognition techniques promise to be very effective, At present, we can not guarantee an excellent identification rate based on a single biometric signature with unimodal biometric systems. Thus the error rates of unimodal biometric systems are relatively high due to all these practical problems, which makes them impractical for the use of critical safety applications. To resolve these problems, a solution is used in the same system in several biometric modalities, called a multimodal biometric system (MBS). MBSs combine different modalities in a unique recognition system. The multimodal fusion allows improving the results obtained by a single biometric characteristic and making the system more robust to noise and interference and more resistant to possible attacks. Fusion may be carried out at the level of signals acquired by the different sensors, of the parameters obtained for each modality, of the scores provided by unimodal experts or of the decision taken by said experts. In the case of fusion, the features obtained from the various biometric methods must be homogenized before the process of fusion is accomplished. This article describes the evolution of a multi-modal biometric identification system depends on 3 biometrics-face, iris & fingerprint. Feature extraction is done using the Gabor Wavelet method and classification is accomplished using the Random Forest classifier. This proposed method is applicable in real-life applications to identify biometric for offices, hospitals, and colleges/universities and so on.

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 A multi-instance multi-sample palmprint identification system

2019 ◽  
Vol 13 (2) ◽  
pp. 825
Author(s):  
Thulfiqar H. Mandeel ◽  
Muhammad Imran Ahmad ◽  
Said Amirul Anwar
Keyword(s):  
Recognition System ◽  
Identification System ◽  
Biometric Recognition ◽  
Linear Discriminant ◽  
Identification Rate ◽  
Palmprint Identification ◽  
Extra Information ◽  
Extraction Algorithm ◽  
Level Fusion ◽  
Method Show

<span>The multibiometric recognition system considered more reliable than the unimodal biometric recognition system due to the addition of an extra information that increases the discrimination between the classes. In this paper, a multi-sample multi-instance biometric recognition system is proposed. The aim of the proposed system is to increase the robustness of the identification. the proposed system also addresses the overfitting to the train samples problem of a feature extraction algorithm, named 2-Dimensional Linear Discriminant analysis (2D-LDA). The samples in the proposed method are bootstrapped and the 2D-LDA performed on each group during the offline phase. While in the online phase, the tested class will be transformed into subspaces using different eigenvectors that obtained from different samplings, and the results matched with templates in the corresponding subspace. To evaluate the proposed method, two palmprint databases are used which are IIT Delhi Touchless Palmprint Database and PolyU palmprint database, and different rank-level fusion algorithms are investigated. The results of the proposed method show improvement in the identification rate.</span>

scholarly journals Attacks on Biometric Systems: An Overview

2015 ◽  
Vol 1 (7) ◽  
pp. 283 ◽  
Author(s):  
Rubal Jain ◽  
Chander Kant
Keyword(s):  
Pattern Recognition ◽  
System Architecture ◽  
Recognition System ◽  
Biometric System ◽  
Knowledge Based ◽  
Biometric Template ◽  
Biometric Systems ◽  
Template Protection ◽  
Authentication System ◽  
Biometric Template Protection

Biometrics is a pattern recognition system that refers to the use of different physiological (face, fingerprints, etc.) and behavioral (voice, gait etc.) traits for identification and verification purposes. A biometrics-based personal authentication system has numerous advantages over traditional systems such as token-based (e.g., ID cards) or knowledge-based (e.g., password) but they are at the risk of attacks. This paper presents a literature review of attack system architecture and makes progress towards various attack points in biometric system. These attacks may compromise the template resulting in reducing the security of the system and motivates to study existing biometric template protection techniques to resist these attacks.

scholarly journals DATABASE MANAGEMENT FOR FACIAL RECOGNITION SYSTEM

2020 ◽  
pp. 128-131
Author(s):  
Sk. Naveed ◽  
N.Ramya ◽  
D.Manasa ◽  
N. Ramya Sri
Keyword(s):  
Face Recognition ◽  
Personal Characteristics ◽  
Recognition System ◽  
Personal Identification ◽  
Identification System ◽  
Training Images ◽  
The Face ◽  
Facial Recognition System ◽  
The Individual ◽  
And Training

The face is one of the easiest ways to differentiate the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics or facial features of a person to identify the person's identity. The most used human face recognition process is face detection ,where this procedure takes place very quickly in humans, except under certain conditions where the object is located at close distance. The purpose of this project is to develop face recognition based automated student attendance system. In order to achieve high quality performance, the test images and training images of this proposed approach are limited to frontal and upright facial images that consist of a single face only. The test images and training images have to be captured by using the same device to ensure no quality difference. In addition, the students have to register in the database to be recognized. The enrolment can be done on the spot through the user-friendly interface.

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.

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