Template Protection Using Multi Biometric Web Modulo Graph

2019 ◽  
Vol 16 (11) ◽  
pp. 4883-4888
Author(s):  
P. Kumaran ◽  
R. Ashoka Rajan ◽  
T. Veeramani ◽  
R. Thilagavathy
Keyword(s):  
Graph Structure ◽  
Biometric Authentication ◽  
Equal Error Rate ◽  
Feature Vectors ◽  
Correct Sequence ◽  
Template Protection ◽  
Novel Method ◽  
Authentication System ◽  
Palm Print ◽  
Enrollment Process

To develop a complete biometric authentication system, security is highly needed. Even though there are several methods for storing fingerprint templates, they are compromised by the attacker leaving it as an unprotected system. In this paper, a novel method is proposed for protecting biometrics through an user defined graph named Web Modulo Graph. Feature vectors are extracted from the Left Fingerprint, Right Fingerprint and Palm Print during the enrollment process. The captured information from the biometrics are combined and stored in Web Modulo Graph where the insertion and traversal of feature vectors are unknown to the attacker. So even if the database or the graph structure is stolen by the attacker the correct sequence cannot be obtained. In this case, guessing the correct sequence is not almost possible as user defined graph is used and the system can achieve this with an Equal Error Rate (EER) of 4.8%. After various analyses, the proposed system is found to have high computational hardness.

scholarly journals Combining Cryptography with EEG Biometrics

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Robertas Damaševičius ◽  
Rytis Maskeliūnas ◽  
Egidijus Kazanavičius ◽  
Marcin Woźniak
Keyword(s):  
Error Rate ◽  
User Authentication ◽  
Discrete Logarithm ◽  
Security Analysis ◽  
Experimental Results ◽  
Biometric Authentication ◽  
Equal Error Rate ◽  
Eeg Data ◽  
Authentication System ◽  
Biometric Cryptosystem

Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

scholarly journals Identity Vector Extraction by Perceptual Wavelet Packet Entropy and Convolutional Neural Network for Voice Authentication

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 600 ◽  
Author(s):  
Lei Lei ◽  
Kun She
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Wavelet Packet ◽  
Vector Model ◽  
Noisy Environment ◽  
Equal Error Rate ◽  
Speech Corpus ◽  
Wavelet Entropy ◽  
Feature Vectors ◽  
Authentication System

Recently, the accuracy of voice authentication system has increased significantly due to the successful application of the identity vector (i-vector) model. This paper proposes a new method for i-vector extraction. In the method, a perceptual wavelet packet transform (PWPT) is designed to convert speech utterances into wavelet entropy feature vectors, and a Convolutional Neural Network (CNN) is designed to estimate the frame posteriors of the wavelet entropy feature vectors. In the end, i-vector is extracted based on those frame posteriors. TIMIT and VoxCeleb speech corpus are used for experiments and the experimental results show that the proposed method can extract appropriate i-vector which reduces the equal error rate (EER) and improve the accuracy of voice authentication system in clean and noisy environment.

Fusion Time Reduction of a Feature Level Based Multimodal Biometric Authentication System

2020 ◽  
Vol 12 (1) ◽  
pp. 67-83
Author(s):  
Rasha O. Mahmoud ◽  
Mazen M. Selim ◽  
Omar A. Muhi
Keyword(s):  
Canonical Correlation ◽  
Recognition Accuracy ◽  
Biometric Authentication ◽  
Equal Error Rate ◽  
Histogram Of Oriented Gradient ◽  
The Face ◽  
Left And Right ◽  
Authentication System ◽  
Correlation Process ◽  
Level Fusion

In the present study, a multimodal biometric authentication method is presented to confirm the identity of a person based on his face and iris features. This method depends on multiple biometric techniques that combine face and iris (left and right) features to recognize. The authors have designed and applied a system to identify people. It depends on extracting the features of the face using Rectangle Histogram of Oriented Gradient (R-HOG). The study applies a feature-level fusion using a novel fusion method which employs both the canonical correlation process and the proposed serial concatenation. A deep belief network was used for the recognition process. The performance of the proposed systems was validated and evaluated through a set of experiments on SDUMLA-HMT database. The results were compared with others, and have shown that the fusion time has been reduced by about 34.5%. The proposed system has also succeeded in achieving a lower equal error rate (EER), and a recognition accuracy up to 99%.

scholarly journals A Survey Paper on Palm Prints Based Biometric Authentication System

2013 ◽  
pp. 188-195
Author(s):  
Swati Verma ◽  
Pomona Mishra
Keyword(s):  
Biometric Authentication ◽  
Human Beings ◽  
Survey Paper ◽  
Powerful Means ◽  
Personal Recognition ◽  
Rich Information ◽  
Authentication System ◽  
Palm Print ◽  
The Rich ◽  
Time Invariant

In this paper we are providing an approach for authentication using palm prints. Reliability in computer aided personal authentication is becoming increasingly important in the information-based world, for effective security system. Biometrics is physiological characteristics of human beings, unique for every individual that are usually time invariant and easy to acquire. Palm print is one of the relatively new physiological biometrics due to its stable and unique characteristics. The rich information of palm print offers one of the powerful means in personal recognition.

scholarly journals Recognizable Proof and Analysis of Palm Print in Biometric Authentication System using Bayes Techniques

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1126-1129
Keyword(s):  
Similar Data ◽  
Biometric Authentication ◽  
Individual Personality ◽  
Authentication System ◽  
Palm Print ◽  
Entire Picture

ID and Analysis Of Palm Print In Biometric Authentication System Using Bayes TechniquesIn nowadays there is a hazard that others can get to a similar data anyplace and whenever. At present passwords, individual recognizable proof cards are utilized for individual ID. Presently a days Biometric based acknowledgment is the most prevalent human acknowledgment design. Biometrics estimates person's one of a kind or conduct attributes to validate individual personality. It gives progressively dependable and proficient methods for character check. The physical component of hand known as palm geometry contains data that is fit for confirming the personality of a person. The objective of biometrics check framework comprises in choosing whether two attributes have a place with same individual or not.In this case picture can be utilized for confirmation purposes. The term worldwide states that entire picture of the palm is considered for confirmation. Principle work for this situation is the pre-handling of picture, at that point extricating the highlights, making the informational index, bunching calculation and Classification calculation is utilized and execution is looked at in both the cases.

Sign in / Sign up

Export Citation Format

Share Document