i-vector Evaluation of Electrocardiogram (ECG) Biometric Identification System based on Sequential Compensation Approach

In all authentication systems, biometric samples are regarded to be the most reliable one. Biometric samples like fingerprint, retina etc. is unique. Most commonly available biometric system prefers these samples as reliable inputs. In a biometric authentication system, the design of decision support system is critical and it determines success or failure. Here, we propose such a system based on neuro and fuzzy system. Neuro systems formulated using Artificial Neural Network learn from numeric data while fuzzy based approaches can track finite variations in the environment. Thus NFS systems formed using ANN and fuzzy system demonstrate adaptive, numeric and qualitative processing based learning. These attributes have motivated the formulation of an adaptive neuro fuzzy inference system which is used as a DSS of a biometric authenticable system. The experimental results show that the system is reliable and can be considered to be a part of an actual design.

Download Full-text

Advances in Biometrics for Secure Human Authentication System

Biometrics ◽

10.4018/978-1-5225-0983-7.ch077 ◽

2017 ◽

pp. 1834-1852

Author(s):

Jagannath Mohan ◽

Adalarasu Kanagasabai ◽

Vetrivelan Pandu

Keyword(s):

Recent Decade ◽

Biometric Identification ◽

Multimodal Biometrics ◽

Biometric Analysis ◽

Multifactor Authentication ◽

Potential Alternative ◽

Authentication System ◽

Identification Techniques ◽

Electrocardiogram Ecg ◽

Pcg Signals

In the recent decade, one of our major concerns in the global technological society of information security is confirmation that a person accessing confidential information is authorized to perform so. Such mode of access is generally accomplished by a person's confirming their identity by the use of some method of authentication system. In present days, the requirement for safe security in storing individual information has been developing rapidly and among the potential alternative is implementing innovative biometric identification techniques. This chapter discusses how the advent of the 20th century has brought forth the security principles of identification and authentication in the field of biometric analysis. The chapter reviews vulnerabilities in biometric authentication and issues in system implementation. The chapter also proposes the multifactor authentication and the use of multimodal biometrics, i.e., the combination of Electrocardiogram (ECG) and Phonocardiogram (PCG) signals to enhance reliability in the authentication process.

Download Full-text

The Biometric Identification System Based on Iris Code

2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T) ◽

10.1109/picst47496.2019.9061270 ◽

2019 ◽

Author(s):

Olexander Lemeshko ◽

Igor Dobrynin ◽

Andriy Zhuravka

Keyword(s):

Biometric Identification ◽

Identification System

Download Full-text

An accurate multi-modal biometric identification system for person identification via fusion of face and finger print

World Wide Web ◽

10.1007/s11280-019-00698-6 ◽

2019 ◽

Vol 23 (2) ◽

pp. 1299-1317 ◽

Cited By ~ 2

Author(s):

Sidra Aleem ◽

Po Yang ◽

Saleha Masood ◽

Ping Li ◽

Bin Sheng

Keyword(s):

Biometric Identification ◽

Identification System ◽

Person Identification ◽

Finger Print

Download Full-text

Identification, Secrecy, Template, and Privacy-Leakage of Biometric Identification System under Noisy Enrollment

2019 IEEE International Symposium on Information Theory (ISIT) ◽

10.1109/isit.2019.8849840 ◽

2019 ◽

Cited By ~ 2

Author(s):

Vamoua Yachongka ◽

Hideki Yagi

Keyword(s):

Biometric Identification ◽

Identification System ◽

Privacy Leakage

Download Full-text

HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition

Sensor Review ◽

10.1108/sr-09-2017-0203 ◽

2020 ◽

Vol 40 (2) ◽

pp. 203-216

Author(s):

S. Veluchamy ◽

L.R. Karlmarx

Keyword(s):

Research Field ◽

Biometric Identification ◽

Svm Classifier ◽

Identification System ◽

Biometric Recognition ◽

Data Set ◽

Content Type ◽

Proposed Model ◽

Simulation Results ◽

Multimodal Biometric Recognition

Purpose Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features. Design/methodology/approach The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector. Findings Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1. Originality/value In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

Download Full-text