Personal Authentication Method Research Articles

Express method of biometric person authentication based on one cycle of the ECG signal

The article is devoted to an express method of biometric authentication of a person based on an electrocardiogram (ECG). The method is characterized by high accuracy (efficiency) of authentication of a person based on only one cycle of its ECG. Such characteristics as Accuracy, Balanced Accuracy and F1-score on average are not lower than 96.1% for such binary classifiers as k-Nearest Neighbors, Linear SVM, Decision Tree, Random Forest, Multilayer Perceptron, Adaptive Boosting, Naive Bayes and Statistical Interval Classifier. The research utilized the Combined Measurement of ECG, Breathing, and Seismocardiograms database, which features data from 20 healthy people. A method of constructing confidence intervals for ECG cycles has been developed, which is based on the rhythm-adaptive statistical estimation of the mathematical expectation and the standard deviation of the ECG signal. The method of constructing confidence intervals is based on the functioning of the Statistical Interval Classifier in the system of biometric authentication of a person. The Statistical Interval Classifier has the lowest time computational complexity among the 8 studied classifiers, which justifies its use in portable biometric authentication systems that have negligible computing resources.

An evaluation of transfer learning models in EEG-based authentication

Electroencephalogram(EEG)-based authentication has received increasing attention from researchers as they believe it could serve as an alternative to more conventional personal authentication methods. Unfortunately, EEG signals are non-stationary and could be easily contaminated by noise and artifacts. Therefore, further processing of data analysis is needed to retrieve useful information. Various machine learning approaches have been proposed and implemented in the EEG-based domain, with deep learning being the most current trend. However, retaining the performance of a deep learning model requires substantial computational effort and a vast amount of data, especially when the models go deeper to generate consistent results. Deep learning models trained with small data sets from scratch may experience an overfitting issue. Transfer learning becomes an alternative solution. It is a technique to recognize and apply the knowledge and skills learned from the previous tasks to a new domain with limited training data. This study attempts to explore the applicability of transferring various pre-trained models’ knowledge to the EEG-based authentication domain. A self-collected database that consists of 30 subjects was utilized in the analysis. The database enrolment is divided into two sessions, with each session producing two sets of EEG recording data. The frequency spectrums of the preprocessed EEG signals are extracted and fed into the pre-trained models as the input data. Three experimental tests are carried out and the best performance is reported with accuracy in the range of 99.1–99.9%. The acquired results demonstrate the efficiency of transfer learning in authenticating an individual in this domain.

Effect of pupil dilation on biometric iris recognition systems for personal authentication.

To study the effect of pupil dilation on a biometric iris recognition (BIR) system for personal authentication and identification. A prospective, non-randomized, single-center cohort study was conducted on patients who reported for a routine eye check-up from November 2017 to November 2019 (2 years). An iris scanning device "IRITECH-MK2120U" was used to initially enroll the undilated eyes. Baseline scans were taken after matching with the enrolled database. All eyes were topically dilated and matched again with the enrolled database. The Hamming distance (a measure of disagreement between two iris codes) and recognition status were recorded from the device output, and eyes were evaluated by slit-lamp ophthalmoscopy with special emphasis on pupil shape, size, and texture. All 321 enrolled eyes matched after topical dilation. The pupil size had a significant effect on Hamming distance with a P value <0.05. There were no false matches. A correct recognition rate of 100% was obtained after dilation. No loss of iris texture or pupil shape was observed after dilation. A BIR system is a reliable method for identification and personal authentication after pupil dilation. Topically dilated pupils are not a cause for non-recognition of iris scans.

Palm vein recognition based on competitive coding scheme using multi-scale local binary pattern with ant colony optimization

Among the various biometric traits that can be extracted from the hand, the palm vein structure that represents a reliable and secure source for identifying and/or verifying the identity of a person. Several recognition methods were proposed in the literature exploiting this modality; among them, the attractive approaches based on a competitive coding. Aiming to further improve the performance of these approaches, this paper presents a novel palm vein recognition method for personal authentication and identification based on a competitive coding scheme using Multi-scale local binary pattern (MLBP) with Ant colony optimization (ACO). ACO allows to override potential blocking points related to image quality or contrast problems that can be encountered with images from the Near infrared spectral band. The pre-processed images will be then sorted with a competitive coding scheme using MLBP; where the final image will be composed of the winning code from the different MLBP images. The matching process for making-decision is then performed using Kullback-Leibler divergence and Jaccard distance. The experimental results obtained on MS-PolyU database has shown that the proposed method achieves improved performances for both identification and verification modes up to 99.64% in terms of CIR for the identification and 0.00078% in terms of EER for the verification; and also outperforms the state-of-the-art methods.

Palm and Fingerprint Based Multimodal Biometric Technique

The biometric based personal authentication method is a technique used for computing and analyzing the physical and/or behavioral characteristics of the person. The in future the biometric system may provide an alternative of digital solution to the ID cards, passwords, driving licenses, passports, etc. Nowadays the researcher and scientist are given interest on multimodal biometric because multimodal biometric gives more security and reliability over the single biometric technique. In this paper, we proposed a multimodal biometric technique based one palm and fingerprint. The IITD palmprint and UPEK fingerprint database used for the experimental evolution.

Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

Biometrics provides greater security and usability than conventional personal authentication methods. Fingerprints, facial identification systems and voice recognition systems are the features that biometric systems can use. To improve biometric authentication, the proposed method considered that the input image is iris and fingerprint; at first, pre-processing is performed through histogram equalization for all image inputs to enhance the image quality. Then the extraction process of the feature will be performed. The suggested method uses modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features next to the extracted features to be combined for feature extraction. Then the optimum function is found with the Rider Optimization Algorithm (ROA) for all MLBP, GLCM and DWT. Eventually, the approach suggested is accepted. Deep Neural Network (DNN) performs the proposed authentication process. A DNN is a multilayered artificial neural network between the layers of input and output. The DNN finds the right mathematical manipulation to turn the input into the output, whether it is an acknowledged image or not. Suggested process quality is measured in terms of reliability recognition. In the MATLAB platform, the suggested approach is implemented.

A GEO - Tagging Based Hybrid Mobile Attendance System

Biometrics technologies verify identity through characteristics such as fingerprints, faces, irises, retinal patterns, palm prints, voice, hand-written signatures, and so on. These techniques, which use physical data, are receiving attention as a personal authentication method that is more convenient than conventional methods such as password or ID cards because it uses data taken from measurements and such data is unique to the individual and remains so throughout one’s lifetime. Geo-tagging is the process of adding geographical information to various media in the form of metadata. The data usually consists of coordinates like latitude and longitude, but may even include bearing, altitude, distance and place names. By combining the above services, we can help the public and private sector offices like Survey Department, Revenue Department, Electricity department, Telephone Department, Public Works Department (PWD), Transport Department (NH, KSRTC etc.) to take care of the employee’s attendance by asking them to install this proposed application in their smart phone.

Biometric Authentication through Unification of Finger Dorsal Biometric Traits

This paper presents a novel method for personal authentication of individuals using a deep learning approach. The reported method implements the fusion of two biometric attributes, namely fingernail plates and finger knuckles. Both these biometric traits have been extracted from the index, middle and ring fingers of a database of 890 (5 each from 178 persons) low-resolution hand dorsal images. AlexNet, a pre-trained deep learning model, has been tailored for feature extraction to suit the number of classes in the database. Deep learning features are extracted from both the traits using the customized Convolutional Neural Network (CNN). These features have been fused at the score-level and the rank-level for different combinations. Both these levels of fusion have been executed through well-established rules. Score-level fusion results show that verification can be done quite accurately with this multimodal framework. Results also depict that information fusion of these traits at the rank-level can be optimized to give Rank-1 identification rate of a perfect 100%.

Wearable Bio-Signal(PPG)-Based Personal Authentication Method Using Random Forest and Period Setting Considering the Feature of PPG Signals

Wearable Bio-Signal(PPG)-Based Personal Authentication Method Using Random Forest and Period Setting Considering the Feature of PPG Signals

Examination of personal authentication method achieving shoulder-surfing resistance by combining mouse operation and number matrix

Examination of personal authentication method achieving shoulder-surfing resistance by combining mouse operation and number matrix

광용적맥파를 이용한 개인인증 기술 개발 적용에 대한 연구

생체인증 기술 (biometrics)은 보안 및 개인인증의 측면에서 폭넓게 사용되고 있으나, 복제의 위험, 인식률 저하 등의 문제가 있다. 본 연구에서는 이러한 단점을 보완하는 기술로 광용적맥파 (photoplethysmography, PPG)를 이용한 개인인증 방법을 제안한다. 제안하는 사용자 등록 알고리즘은 계측된 PPG 신호의 6개 특징값을 추출하여 데이터베이스에 저장하는 알고리즘이고, 이 계측된 데이터베이스와 새롭게 측정된 PPG 신호의 특징값을 비교분석하여 사용자를 식별하는 것이 개인인증 알고리즘이다. 이 방법의 타당성을 검증하기 위해 9명의 피험자를 모집하여 실험하였다. 9명의 피험자는 모두 사용자 등록 알고리즘을 시행하고, 그 중 같은 조건에서 참여 가능한 5명의 피험자에 대하여 개인인증을 구현하였다. 총 5명중 4명이 개인인증에 성공하였다. 연구결과를 바탕으로 PPG를 이용한 개인인증 기술이 기존의 생체인증 기술의 단점을 보완하는 방법이 되리라 기대된다.

A Dempster–Shafer theory based classifier combination for online Signature recognition and verification systems

With the advancement in technology, the society demands a robust method for person authentication. Traditional authentication methods are based on the person’s knowledge such as PIN, passwords, and tokens etc. However, such methods are prone to steal and forgotten risks. Therefore, an efficient method for person identification and verification is required. In this paper, we present a novel biometric approach for online handwritten signature recognition and verification using Dempster–Shafer theory (DST). DST has been used effectively for combination of different information sources which provide incomplete, and complementary knowledge. Initially, signature identification and verification processes have been carried out using two different classifiers, namely, Hidden Markov Model (HMM) and Support Vector Machine (SVM). Next, the performance in terms of accuracy and the reliability of the system has been increased using DST by combining the probabilistic outputs of SVM and HMM classifiers. The feasibility of the approach has been tested on MCYT DB1 and SVC2004 biometric public databases for Latin script and a new online signature dataset for Devanagari script. To our knowledge there exist no dataset on online signature available in Devanagari script. Experimental results shows that the present approach is efficient in recognition and verification of signatures and outstrips existing work in this regard till date.

Gait based biometric personal authentication by using MEMS inertial sensors

Walking is one of the major human activities, and walking pattern is unique for each individual. Thus, human gait can be applied in biometric personal authentication. The traditional method for gait recognition is based on one or multiple cameras. With the rapid development of Micro-Electro-Mechanical System (MEMS), small light inertial sensors have been used for human identification so far. In this study, a gait based personal authentication method is proposed using MEMS inertial sensors. They are fixed in the smart shoes, collecting motion signals and transmitting them to the server. Then, gait parameters such as step length, cadence, stance phase, swing phase and the pitch angular are calculated and used as features for personal identification. A probabilistic neural network is proposed as a classification mechanism to uniquely identify different users. Experiments are conducted to validate the proposed method. By using two cross-validation techniques, the overall mean classification rate for 22 persons is up to 85.3 and 85.7% respectively, which demonstrates the effectiveness of the method.

Safe and convenient personal authentication method using Moiré 3D authentication based on biometric authentication

With the advancement of IT, users can now share and use a vast amount of information, but at the same time, their personal information has been widely used and made public. The personal information used should be protected in a secure manner, and users should also go through the correct authentication process. For this, user biometric information is being used as an authentication method. Currently, there are various methods of authenticating using biometric information of a user. However, to build a secure authentication system, it is necessary to use a method with high entropy, which is easy to use and a measure of unpredictability. In a biometric authentication protocol currently used in many systems, biometric information required for authentication is stored and used in a certificate authority or a web server. This can result in the occurrence of information leakage. The paper utilizes data that can be used for passwords extracted using the Moire technique to apply it to extracting user biometric information to propose a safer and convenient method of authentication. Also, it proposes an authentication protocol that can authenticate a user through a combination of user personal information and data extracted through Moire technique using multi-factor authentication technology, without storing the data on the server of a certification authority.

Exploring finger vein based personal authentication for secure IoT

Personal authentication is getting harder and harder in the internet of things (IoT). Existing methods used for personal authentication, such as passwords and the two-factor authentication (2FA), are inadequate and ineffective due to human error and other attacks. To support more secure IoT, this paper proposes a finger vein based personal authentication method by exploring competitive orientations and magnitudes from finger vein images. Finger vein recognition has been proven to be a reliable and promising solution for biometric-based personal authentication. The stable and rich piecewise line features in finger vein images can be used to clearly represent finger vein patterns for personal authentication. In this paper, we propose an efficient local descriptor for finger vein feature extraction, namely the histogram of competitive orientations and magnitudes (HCOM). For a finger vein image, two types of local histograms are extracted and fused together to efficiently and adequately represent the competitive information: the histogram of competitive orientations (HCO) and the local binary pattern histogram generated from the image of competitive magnitudes (named as HCMLBP). The extensive experimental results from the application of the proposed method to the public finger vein database MMCBNU_6000, demonstrate that the proposed method outperforms state-of-the-art orientation coding (OC)-based methods and other commonly used local descriptors. Additionally, the proposed method can be used for finger vein image enhancement.

An evaluation of personal authentication methods in biometrics

An evaluation of personal authentication methods in biometrics

곡률기반 기준점 검출을 이용한 계층적 심전도 신호 개인인증 알고리즘

Electrocardiogram(ECG) signal is one of the unique bio-signals of individuals and is used for personal authentication. The existing studies on personal authentication method using ECG signals show a high detection rate for a small group of candidates, but a low detection rate and increased execution time for a large group of candidates. In this paper, we propose a hierarchical algorithm that extracts fiducial points based on curvature of ECG signals as feature values for grouping candidates ​and identifies candidates using waveform-based comparisons. As a result of experiments on 74 ECG signal records of QT-DB provided by Physionet, the detection rate was about 97% at 3-heartbeat input and about 99% at 5-heartbeat input. The average execution time was 22.4 milliseconds. In conclusion, the proposed method improves the detection rate by the hierarchical personal authentication process, and also shows reduced amount of computation which is plausible in real-time personal authentication usage in the future.

An Efficient Integrator based on Template Matching Technique for Person Authentication using Different Biometrics

Objectives: A boosting based multiple classifier system has been developed using different biometric features like finger-print, palm-print, wrist-vein and handwriting for person authentication. Methods/Statistical Analysis: The multi-classifier is comprised of template matching based four different classifiers. These individual classifiers identify fingerprints, palm-prints, wrist-veins and handwritings separately and the super-classifier performs combination of four conclusions to set up the final decision based on programming based boosting method for person authentication. Findings: A new concept of Programming Based Boosting has been introduced in the super-classifier of this system to ultimately perform accurate person authentication with a variety of biometrics. This achieves better decision or conclusion regarding person identification/authentication than that with conventional single classifier with one or two biometrics or multimodal classifiers with conventional bagging or boosting. The method of utilizing multiple classifiers with four different biometric features in a single system is productive and viable as it would be difficult for an impostor to spoof all four different biometrics of a genuine user simultaneously. Also the accuracy, precision, recall and F-score of the classifiers are substantially moderate and the training and testing time of all the biometrics are quite low and affordable. Application/Improvements: This system will extremely helpful in such applications where accurate but rapid human identification is required like at the time of ATM transactions, VIP office entrance, access to computers or emails or e-bank account etc.

Proof-of-concept and evaluation of a dual function visible/NIR camera for iris authentication in smartphones

As a robust method of person authentication, iris biometrics is making its way in to consumer devices such as smartphones. Current iris image acquisition devices typically work under controlled environment and constrained acquisition conditions. In this paper, the adaption of iris biometrics for unconstrained, hand-held devices such as smartphones is investigated. A prototype device is presented with full system description. This device is equipped with a single image sensor with both visible and NIR sensing capabilities. The device is analysed in terms of its optical properties and iris imaging capabilities. Preliminary results indicate that there are challenges to achieve a reliable recognition performance from the images captured using this device. Current system acquires images with marginal optical quality and spatial resolution in an unconstrained acquisition scenario for iris recognition. Nevertheless, the analyses presented in this paper indicate a similar camera module with improved optics and sensor could combine iris biometrics with conventional front camera functions such as video call and the capture of selfie images.

Capacitive Fingerprint Sensor Circuit using Pseudo-Direct Scheme for Mobile Applications

This paper proposes a fingerprint sensor cell and its structure using pseudo-direct scheme. The fingerprint sensor cell uses an internal transmitted (Tx) plate instead of using an external bezel as a contact for direct signal driving to finger. It uses an active output voltage feedback (AOVF) integrator and multiple integration scheme to maximize the sensitivity and improve signal-to-noise ratio (SNR) as well. The circuit models of combined fingerprint and Tx electrode were analyzed and simulated using MATLAB. And the proposed fingerprint sensor were designed and simulated with standard 0.35μm CMOS technology. Simulations verified that the proposed fingerprint sensor using pseudo-direct scheme can be effectively used as a personal authentication method especially in mobile applications.