BIOMETRIC AUTHENTICATION METHODS FOR PERSONAL IDENTIFICATION

Biometric offers potential for automatic personal identification and verification, differently from other means for personal verification; biometric means are not based on the possession of anything (as cards) or the knowledge of some information (as passwords). There is considerable interest in biometric authentication based on automatic signature verification (ASV) systems because ASV has demonstrated to be superior to many other biometric authentication techniques e.g. finger prints or retinal patterns, which are reliable but much more intrusive and expensive. An ASV system is a system capable of efficiently addressing the task of make a decision whether a signature is genuine or forger. Numerous pattern recognition methods have been applied to signature verification. Among the methods that have been proposed for pattern recognition on ASV, two broad categories can be identified: memory-based and parameter-based methods as a neural network. The Major approaches to ASV systems are the template matching approach, spectrum approach, spectrum analysis approach, neural networks approach, cognitive approach and fractal approach. The proposed article reviews ASV techniques corresponding with approaches that have so far been proposed in the literature. An attempt is made to describe important techniques especially those involving ANNs and assess their performance based on published literature. The paper also discusses possible future areas for research using ASV.

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IMPROVEMENT OF THE CREDIBILITY OF ANALYSIS OF ELECTROCARDIOGRAMS FOR BIOMETRIC PERSONAL IDENTIFICATION

Перспективні технології та прилади ◽

10.36910/6775-2313-5352-2019-15-18 ◽

2020 ◽

pp. 125-130

Author(s):

Яковенко І.О. ◽

Рудий О.Д. ◽

Турчина М.О.

Keyword(s):

Electrical Activity ◽

Age Groups ◽

Personal Identification ◽

Physical Parameters ◽

Biometric Authentication ◽

High Demand ◽

Biometric Data ◽

Ecg Signals ◽

High Level ◽

Electrocardiogram Ecg

Nowadays there is a high demand for biometric authentication. These systems possess a high level of protection, as they evaluate not only the physical parameters, but also personality characteristics. The paper analyzes a biometric scheme based on the electrical activity of the human heart in the form of electrocardiogram (ECG) signals. The study was performed using standard laboratory measurements KL-720 has all age groups. As a result, an electrical activity signal was obtained. The aim of this work was to filter the captured signal for further use with biometric data.

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AUTOMATIC PALMPRINT VERIFICATION

International Journal of Image and Graphics ◽

10.1142/s0219467801000104 ◽

2001 ◽

Vol 01 (01) ◽

pp. 135-151 ◽

Cited By ~ 44

Author(s):

WEI SHU ◽

GANG RONG ◽

ZHAOQI BIAN ◽

DAVID ZHANG

Keyword(s):

Feature Extraction ◽

Personal Identification ◽

Classification Method ◽

Projection Algorithm ◽

Biometric Authentication ◽

Determination Method ◽

Matching Method ◽

Line Feature ◽

Remarkable Advantage

Automic palmprint verification is an important complement of biometric authentication. As the first attempt of personal identification by palmprint, this paper explores different methods for three main processing stages in palmprint verification including datum point registration, line feature extraction and palmprint classification. The datum points of palmprint which have the remarkable advantage of invariable location are defined and their determination method using the directional projection algorithm is improved. Then, line feature extraction and line matching method is described to detect whether a couple of palmprints are from the same palm. In addition, palmprint classification method based on the orientation property of the ridges is discussed to distinguish six typical cases. Various palmprint images have been tested to illustrate the effectiveness of the proposed methods.

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Multi-Factor Authentication and Fingerprint-based Debit Card System

U Porto Journal of Engineering ◽

10.24840/2183-6493_005.002_0003 ◽

2019 ◽

Vol 5 (2) ◽

pp. 19-28

Author(s):

Mubarak Adetunji Ojewale ◽

Patrick Meumeu Yomsi

Keyword(s):

Mobile Phone ◽

Personal Identification ◽

Biometric Authentication ◽

Personal Identification Number ◽

Debit Card ◽

Automated Teller Machines ◽

Card System ◽

Authentication System ◽

One Time Password ◽

Secure Authentication

One thing can be said to be common to all forms of debit card fraud – authentication bypass. This implies that a secure debit card transaction system can only be guaranteed by a safe and reliable authentication system. Many approaches have been adopted to ensure a secure authentication system, but often, these approaches are either focused on the Automated Teller Machines (ATM)/Point of Sales (POS) terminals or Online/e-commerce transactions, thus not providing full security on both fronts. In this work, we address this problem by adopting a multi-factor debit card system that uses a combination of the traditional Personal Identification Number code (PIN) and the mobile-phone delivered One-Time Password (OTP) with a biometric authentication option (fingerprint). We demonstrate that this approach ensures the security of both online and terminal transactions. The fingerprint option makes it easy to use by people who find memorizing PINs difficult.

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Keystroke Dynamics-Based Authentication Using Unique Keypad

Sensors ◽

10.3390/s21062242 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2242

Author(s):

Maro Choi ◽

Shincheol Lee ◽

Minjae Jo ◽

Ji Sun Shin

Keyword(s):

User Authentication ◽

Classification Performance ◽

Performance Outcomes ◽

Error Rates ◽

Personal Identification ◽

Biometric Authentication ◽

Keystroke Dynamics ◽

User Classification ◽

Wide Range ◽

Filtering Rates

Authentication methods using personal identification number (PIN) and unlock patterns are widely used in smartphone user authentication. However, these authentication methods are vulnerable to shoulder-surfing attacks, and PIN authentication, in particular, is poor in terms of security because PINs are short in length with just four to six digits. A wide range of research is currently underway to examine various biometric authentication methods, for example, using the user’s face, fingerprint, or iris information. However, such authentication methods provide PIN-based authentication as a type of backup authentication to prepare for when the maximum set number of authentication failures is exceeded during the authentication process such that the security of biometric authentication equates to the security of PIN-based authentication. In order to overcome this limitation, research has been conducted on keystroke dynamics-based authentication, where users are classified by analyzing their typing patterns while they are entering their PIN. As a result, a wide range of methods for improving the ability to distinguish the normal user from abnormal ones have been proposed, using the typing patterns captured during the user’s PIN input. In this paper, we propose unique keypads that are assigned to and used by only normal users of smartphones to improve the user classification performance capabilities of existing keypads. The proposed keypads are formed by randomly generated numbers based on the Mersenne Twister algorithm. In an attempt to demonstrate the superior classification performance of the proposed unique keypad compared to existing keypads, all tests except for the keypad type were conducted under the same conditions in earlier work, including collection-related features and feature selection methods. Our experimental results show that when the filtering rates are 10%, 20%, 30%, 40%, and 50%, the corresponding equal error rates (EERs) for the proposed keypads are improved by 4.15%, 3.11%, 2.77%, 3.37% and 3.53% on average compared to the classification performance outcomes in earlier work.

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Point of Sale (POS) Network with Embedded Fingerprint Biometric Authentication

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst119659 ◽

2019 ◽

pp. 95-111 ◽

Cited By ~ 11

Author(s):

Hussah Adnan Alzame ◽

Muneerah Alshabanah ◽

Mutasem K. Alsmadi

Keyword(s):

Personal Identification ◽

Security Level ◽

Biometric Authentication ◽

Social Security Number ◽

Point Of Sale ◽

Automated Teller Machines ◽

Steady Growth ◽

Electronic Transactions ◽

Automated Teller Machine ◽

Financial Transactions

The steady growth in electronic transactions has promoted the Automated Teller Machine (ATM) thereby making it the main transaction channel for carrying out financial transactions. Conventional method of identification based on possession of ID cards or exclusive knowledge like a social security number or a password are not all together reliable. However, this has also increased the amount of fraudulent activities carried out on Automated Teller Machines (ATMs) thereby calling for efficient security mechanisms and increasing the demand for fast and accurate user identification and authentication in ATMs. In this paper, an embedded fingerprint biometric authentication scheme for Point of Sale (POS) network as additional security option to the ATM card is proposed. A fingerprint biometric technique was fused with personal identification numbers (PIN's) for authentication to upgrade the security level. The proposed idea will solve the problems that may face the customers such as theft, counterfeiting, oblivion or loss the card. Therefore, the customer will be identified once putting his finger on the reader (based on finger scanning) and the system recognizes the customer without requiring keys or cards of support. We have distributed a questioner to 586 responders, the obtained results show the importance of fingerprint biometric authentication for POS network as additional security option to the ATM card.

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Cosmetic applied based face recognition for biometric passport

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-02-2019-0016 ◽

2019 ◽

Vol 8 (1) ◽

pp. 3-22

Author(s):

Ziaul Haque Choudhury ◽

M. Munir Ahamed Rabbani

Keyword(s):

Face Recognition ◽

National Security ◽

International Crime ◽

Principal Component ◽

Personal Identification ◽

Gradient Field ◽

Appearance Model ◽

Biometric Authentication ◽

Content Type ◽

Verification Process

Purpose Nowadays, the use of forged e-passport is increasing, which is threatening national security. It is important to improve the national security against international crime or terrorism. There is a weak verification process caused by lack of identification processes such as a physical check, biometric check and electronic check. The e-passport can prevent the passport cloning or forging resulting from the illegal immigration. The paper aims to discuss these issues. Design/methodology/approach This paper focuses on face recognition to improve the biometric authentication for an e-passport, and it also introduces facial permanent mark detection from the makeup or cosmetic-applied faces, twins and similar faces. An algorithm is proposed to detect the cosmetic-applied facial permanent marks such as mole, freckle, birthmark and pockmark. Active Shape Model into Active Appearance Model using Principal Component Analysis is applied to detect the facial landmarks. Facial permanent marks are detected by applying the Canny edge detector and Gradient Field Histogram of Oriented Gradient. Findings This paper demonstrated an algorithm and proposed facial marks detection from cosmetic or makeup-applied faces for a secure biometric passport in the field of personal identification for national security. It also presented to detect and identify identical twins and similar faces. This paper presented facial marks detection from the cosmetic-applied face, which can be mixed with traditional methods. However, the use of the proposed technique faced some challenges due to the use of cosmetic. The combinations of the algorithm for facial mark recognition matching with classical methods were able to attain lower errors in this proposed experiment. Originality/value The proposed method will enhance the national security and it will improve the biometric authentication for the e-passport. The proposed algorithm is capable of identifying facial marks from cosmetic-applied faces accurately, with less false positives. The proposed technique shows the best results.

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