Biometric Person Authentication Research Articles

ENHANCE DOCUMENT VALIDATION UIPATH POWERED SIGNATURE VERIFICATION

Abstract—Signatures are widely used as a means of personal identification and verification. Many documents like bank cheques and legal transactions require signature verification. Signature-based verification of a large number of documents is a very difficult and time-consuming task. Consequently, an explosive growth has been observed in biometric personal verification and authentication systems that are connected with quantifiable physical unique characteristics (finger prints, hand geometry, face, ear, iris scan, or DNA) or behavioural features (gait, voice etc.). As traditional identity verification methods such as tokens, passwords, pins etc suffer from some fatal flaws and are incapable to satisfy the security necessities, the paper aims to consider a more reliable biometric feature, signature verification for the considering. We present a survey of signature verification systems. We classify and give an account of the various approaches that have been proposed for signature verification.

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

The significant increase in online activities in the wake of recent global events has underlined the importance of biometric person authentication on digital platforms. Although many biometric devices may be used for precise biometric authentication, acquiring the necessary technology, such as 3D sensors or fingerprint scanners, can be prohibitively expensive and logistically challenging. Addressing the demands of online environments, where access to specialized hardware is limited, this paper introduces an innovative approach. In this work, by fusing static and dynamic signature data with facial data captured through regular computer cameras, a dataset of 1750 samples from 25 individuals is constructed. Deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN), are employed to craft a robust multi-classification model. This integration of various deep learning algorithms has demonstrated remarkable performance enhancements in biometric authentication. This research also underscores the potential of merging dynamic and static biometric features, derived from readily available sources, to yield a high-performance recognition framework. As online interactions continue to expand, the combination of various biometric modalities holds potential for enhancing the security and usability of virtual environments.

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.

Identity Verification using Geometry of Human Hands

Biometrics which can be used for identification of individuals based on their physical or behavioural characteristics has gained importance in today’s society where information security is essential. Hand geometry based biometrics systems are gaining acceptance in low to medium security applications. Hand geometry based identification systems utilize the geometric features of the hand like length and width of the fingers, diameter of the palm and the perimeter. The proposed system is a verification system which utilizes these hand geometry features for user authentication. This project introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system. One of the novelties of this work comprises on the introduction of hand geometry’s related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Today students’ (class) attendance became more important part for any organizations/institutions. The conventional method of taking attendance by calling names or signing on paper is very time consuming and insecure, hence inefficient. This paper presents the manual students’ attendance management into computerized system for convenience or data reliability. So, the system is developed by the integration of ubiquitous computing systems into classroom for managing the students’ attendance using palm print scanner. The system is designed to implement an attendance management system based on palm print scanner which students need to use their palm to success the attendance where only authentic student can be recorded the attendance during the class. This system takes attendance electronically with the help of the webcam, and the records the attendance in a database. Students’ roll call percentages and their details are easily seen via Graphical User Interface (GUI).

Batch Hard Contrastive Loss and Its Application to Cross-View Gait Recognition

Biometric person authentication comprises two tasks: the identification task (i.e., one-to-many matching) and the verification task (i.e., one-to-one matching). In this paper, we propose a loss function called batch hard contrastive loss (BHCn) for the deep learning-based verification task. For this purpose, we consider batch mining techniques developed in the identification task and translate them to the verification task. More specifically, inspired by batch mining triplet losses to learn a relative distance for the identification task, we propose BHCn to learn an absolute distance that better represents verification in general. Our method preserves the identity-agnostic nature of the contrastive loss by selecting the hardest pair of samples for each pair of identities in a batch instead of selecting the hardest pair for each sample. We validate the effectiveness of the proposed method in cross-view gait recognition using three networks: a lightweight input, structure, and output network we call GEI + CNN (Gait Energy Image Convolutional Neural Network) as well as the widely used GaitSet and GaitGL, which have sophisticated inputs, structures, and outputs. We trained these networks with the publicly available silhouette-based datasets, the OU-ISIR Gait Database Multi-View Large Population (OU-MVLP) dataset and the Institute of Automation Chinese Academy of Sciences Gait Database Multiview (CASIA-B) dataset. Experimental results show that the proposed BHCn outperforms other loss functions, such as a triplet loss with batch mining as well as the conventional contrastive loss.

“Gesture Identifier”

Abstract: In modern society, where information security is emphasized, the importance of biometrics, which identifies individuals based on their physical and behavioral characteristics, is increasing. Hand geometry based biometric systems are gaining acceptance in low-to-medium security applications. Hand geometry -based identification systems use geometric characteristics of the hand, such as finger length and width, palm diameter and circumference. The proposed system is an authentication system that uses these hand shape features for user authentication. This project presents a low-cost, high-performance, and easy-to-use hand-geometry based biometric personal authentication system. One of the novelties of this work involves the introduction of position-based, position-independent feature extraction and hand shape identification, useful for problems related to image processing and pattern recognition. Today, the presence of students (classes) has become an important part of every organization/institution. Traditional methods of recording attendance through paper attribution or signatures are very time consuming, insecure and inefficient. This paper describes manual student attendance management in a computerized system for convenience or data reliability. Therefore, the system was developed by integrating a ubiquitous computing system into the classroom to manage student attendance with palmprint scanners

Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks

Biometrics is an active area of research because of the increase in need for accurate person identification in numerous applications ranging from entertainment to security. Unimodal and multimodal are the well-known biometric methods. Unimodal biometrics uses one biometric modality of a person for person identification. The performance of an unimodal biometric system is degraded due to certain limitations such as: intra-class variations and nonuniversality. The person identification using more than one biometric modality of a person is multimodal biometrics. This method of identification has gained more interest due to resistance on spoof attacks and more recognition rate. Conventional methods of feature extraction have difficulty in engineering features that are liable to more variations such as illumination, pose and age variations. Feature extraction using convolution neural network (CNN) can overcome these difficulties because large dataset with robust variations can be used for training, where CNN can learn these variations. In this paper, we propose multimodal biometrics at feature level horizontal fusion using face, ear and periocular region biometric modalities and apply deep learning CNN for feature representation and also we propose face, ear and periocular region dataset that are robust to intra-class variations. The evaluation of the system is made by using proposed database. Accuracy, Precision, Recall and [Formula: see text] score are calculated to evaluate the performance of the system and had shown remarkable improvement over existing biometric system.

Multi-modal compound biometric feature set security and person authentication using cancelable 2D color barcode pattern generation technique

Multi-modal compound biometric feature set security and person authentication using cancelable 2D color barcode pattern generation technique

Spoofing Detection and Countermeasure in Automatic Speaker Verification System using Dynamic Features

This present paper aims to extract robust dynamic features used to spoofing detection and countermeasure in ASV system. ASV is a biometric person authentication system. Researchers are aiming to develop spoofing detection and countermeasure techniques to protect this system against different spoofing attacks. For this, replayed attack is considered, because of very common accessibility of recording devices. In replay spoofing, the speech utterances of target (genuine) speakers are recorded and played against ASV system for gaining access unauthorizedly. For this purpose, as a first step, different dynamic features will be extracted for each speech sample. For feature extraction MFCC, LFCC, and MGDCC feature extraction techniques are used. As a second step, a classifier is used to classify whether the given speech sample is genuine or not. As a classifier, GMM and universal background model is used. In this present work, GMM based ASV system and Countermeasure systems using different feature extraction techniques are developed, and the performance of the methods is evaluated using EER and t- DCF. Basing on the performance values, the best feature extraction technique is selected.

Валідність автентифікації людини за електрокардіограмою з обмеженою кількістю каналів

The concept of mobile and home telemedicine for screening and early diagnostics of cardiovascular dis-eases is being expanded due to the emergence of mobile diagnostic devices and smartphones. In the course of such telemedicine consultations, the doctor must be sure that the digital electrocardiogram (ECG) be-longs to the patient who was registered. Both multi-channel and single-channel ECG-recording devices are available on the telemedicine consulting market now. Single-channel electrocardiographs are more eco-nomic feasible for home use. Previously, the authors have developed and experimentally tested the algo-rithms for patient authentication by his/her multi-channel ECG. These algorithms are based on the analysis of the shape of QRS complex in three-dimensional phase space of coordinates. Therefore, it is reasonable to adapt these algorithms to single-channel ECG. In case of multi-channel ECG, we can construct a three-dimensional phase space of coordinates by obtaining all the necessary data from the ECG leads. In a case of the single-channel ECG it is necessary to create two additional signals artificially and then it will be possible to form a synthetic phase space. In general, the question of the validity of biometric person au-thentication algorithms by his/her ECG with a limited number of channels is discussed in this paper. Be-sides the algorithms for solving the problem of authentication, the comparison of sensitivity and specificity indicators, calculated on the results of experiments for multi-channel and single-channel ECG, are also given in this paper. The results of experiments with multi-channel and single-channel ECG of a larger number of patients are given in comparison to the previous experiments. The results of the experiments for the case of recording ECG signals by different devices are given as well.

Biometric person authentication framework using polynomial curve fitting-based ECG feature extractio

The applications of modern biometric techniques for person identification systems rapidly increase for meeting the rising security demands. The distinctive physiological characteristics are more correctly measurable and trustworthy since previous measurements are not appropriately made for physiological properties. While a variety of strategies have been enabled for identification, the electrocardiogram (ECG)-based approaches are popular and reliable techniques in the senses of measurability, singularity, and universal awareness of heartbeat signals. This paper presents a new ECG-based feature extraction method for person identification using a huge amount of ECG recordings. First of all, 1800 heartbeats for each of the 36 subjects have been obtained from the widespread and large MIT-BIH database (MITDB) downloaded from the PhysioBank archive. Then the fiducial points of each heartbeat were determined and fourteen different features were extracted utilizing these fiducial points. Next, the polynomial curve fitting-based dimension reduction technique was employed on the extracted fourteen features. Furthermore, six celebrated classifiers including artificial neural networks (ANNs), decision trees (DTs), Fisher linear discriminant analysis (FLDA), K-nearest neighbors (K-NNs), naive Bayes (NB), and support vector machines (SVMs) were applied for the verification and performance evaluation of the proposed study. Also, as a different classifier, temporal classification and random forest was utilized for a benchmark classification. The highest performance was attained with 95.46\% accuracy rate in the case of the SVM classifier. The experimental results emphasize that the proposed ECG-based feature extraction method gives insightful merit for biometric-based person authentication systems.

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.

Multiple classifiers in biometrics. part 1: Fundamentals and review

We provide an introduction to Multiple Classifier Systems (MCS) including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods. This introduction complements other existing overviews of MCS, as here we also review the most prevalent theoretical framework for MCS and discuss theoretical developments related to MCS.The introduction to MCS is then followed by a review of the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. This review includes general descriptions of successful MCS methods and architectures in order to facilitate the export of them to other information fusion problems.Based on the theory and framework introduced here, in the companion paper we then develop in more technical detail recent trends and developments in MCS from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in the present paper, methods in the companion paper are introduced in a general way so they can be applied to other information fusion problems as well. Finally, also in the companion paper, we discuss open challenges in biometrics and the role of MCS to advance them.

Multiple classifiers in biometrics. Part 2: Trends and challenges

The present paper is Part 2 in this series of two papers. In Part 1 we provided an introduction to Multiple Classifier Systems (MCS) with a focus into the fundamentals: basic nomenclature, key elements, architecture, main methods, and prevalent theory and framework. Part 1 then overviewed the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. Here in Part 2 we present in more technical detail recent trends and developments in MCS coming from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in Part 1, methods here are described in a general way so they can be applied to other information fusion problems as well. Finally, we also discuss here open challenges in biometrics in which MCS can play a key role.

Using the score ratio with distance-based classifiers: A theoretical and practical study in biometric signature recognition

The binary classification problem where an input is classified as belonging or not to a certain class, the so-called Target Class (TC), is approached here. This problem can be stated as a basic hypothesis test: X is from the TC (H0) vs. X is not from the TC (H1), where X is the classifier input. When probabilistic models are used (e.g., Hidden Markov Models or Gaussian Mixture Models), the likelihood ratio, p(X/H0)/p(X/H1), is an alternative widely used to improve the classification. However, as far as we know, this ratio is not usually applied with distance-based classifiers (e.g., Dynamic Time Warping). Following that idea, here we propose making the decision based not only on the score (“score” being the classifier output) assuming X to be from the TC (H0), but also using the score assuming X is not from the TC (H1), by means of the ratio between both scores: the score ratio. The proposal is tested in biometric person authentication using manuscript signature, with three different state-of-the-art systems based on distance classifiers. Different alternatives for applying the proposal are shown in order to reduce the computer load, should it prove necessary. Using the score ratio has led to improvements in most of the tests performed. The best verification results were achieved using our proposal, with the best ones without the score ratio being improved by an average of 22%.

Client threshold prediction in biometric signature recognition by means of Multiple Linear Regression and its use for score normalization

Biometric person authentication has become an important area of fieldwork both for research and commercial purposes in the last few years. The development of the technology, now ready for practical applications, has encouraged the scientific community to focus on practical issues. In this sense, a key question is the decision threshold estimation. Biometric authentication is a pattern recognition problem where a final decision (identity accepted/rejected) must be taken; so, to set a correct decision threshold is essential, since the best system becomes useless if an inaccurate decision threshold is fixed. This work focuses on this subject for biometric systems based on manuscript signatures. The decision threshold can be client (signatory) dependent or the same for all (common threshold). In this paper, new approaches for both problems are shown. A new solution, based on the Multiple Linear Regression model, is proposed for client dependent decision threshold estimation or prediction. The state of the art shows that only independent variables based on the Gaussian scores distribution supposition have been used. Here, new robust parameters, not based on that supposition, have been successfully included in the model. This proposal has been evaluated by means of both a statistical validation and a performance comparison with the state of the art. When a common threshold is used, the problem is to normalize the client scores. A new proposal for this task is also shown, based on the use of the predicted client threshold. Both proposals have been multi-working point, multi-corpus and multi-classifier tested. Improvements from 12% to 57% have been achieved with respect to the state of the art in threshold prediction, while these improvements are from 15% to 40% in the score normalization task.

Multimodal Biometric Person Authentication : A Review

This paper provides a review of multimodal biometric person authentication systems. The paper begins with an introduction to biometrics, its advantages, disadvantages, and authentication system using them. A brief discussion on the selection criteria of different biometrics is also given. This is followed by a discussion on the classification of biometric systems, their strengths, and limitations. Detailed descriptions on the multimodal biometric person authentication system, different modes of operation, and integration scenarios are also provided. Considering the importance of information fusion in multi-biometric approach, a separate section is dedicated on the different levels of fusion, which include sensor-level, feature-level, score-level, rank-level, and abstract-level fusions, and also different rules of fusion. This paper also presents an overview of some performance parameters and error rates for biometric person authentication systems. A separate section is devoted to the recent trends in biometrics field, namely, adaptive biometric system, analysis of complementary and supplementary information, and physiological biometrics. The paper concludes with a discussion on the issues that are currently holding the deployment of multimodal biometric person authentication systems and a possible scope for future work.

Secure speech biometric templates for user authentication

The security of biometrics against attacks is a serious concern in biometric personal authentication systems. In particular, the security of biometric templates is a topic of rapidly growing importance in the area of user authentication. The authors first demonstrate the security of a protected speech biometric template and devised an algorithm to attack a speech biometric user authentication system where templates are protected by a cryptographic framework. The experimental result showed an improvement for attackers in gaining access to the system. Then, a way to combine a password with a speech biometric cryptosystem is proposed. The authors present two schemes to enhance verification performance in a biometric cryptosystem using a password. Both can resist a password brute-force search if the biometrics are not compromised. Even if the biometrics are compromised, the attackers have to make many more attempts in searching for cryptographic keys in the system described in this study, compared to a traditional password-based approach. Finally, it is shown that the error rate of the proposed scheme is the same as in a traditional password-based approach even when genuine biometrics or templates are compromised.

Comparison of clustering methods: A case study of text-independent speaker modeling

Clustering is needed in various applications such as biometric person authentication, speech coding and recognition, image compression and information retrieval. Hundreds of clustering methods have been proposed for the task in various fields but, surprisingly, there are few extensive studies actually comparing them. An important question is how much the choice of a clustering method matters for the final pattern recognition application. Our goal is to provide a thorough experimental comparison of clustering methods for text-independent speaker verification. We consider parametric Gaussian mixture model (GMM) and non-parametric vector quantization (VQ) model using the best known clustering algorithms including iterative ( K-means, random swap, expectation–maximization), hierarchical (pairwise nearest neighbor, split, split-and-merge), evolutionary (genetic algorithm), neural (self-organizing map) and fuzzy (fuzzy C-means) approaches. We study recognition accuracy, processing time, clustering validity, and correlation of clustering quality and recognition accuracy. Experiments from these complementary observations indicate clustering is not a critical task in speaker recognition and the choice of the algorithm should be based on computational complexity and simplicity of the implementation. This is mainly because of three reasons: the data is not clustered, large models are used and only the best algorithms are considered. For low-order models, choice of the algorithm, however, can have a significant effect.

Fuzzy-logic is precise—Its application to biometric system

This paper describes a biometric personal authentication method, using a pair of right and left sole pressure distribution changes, while walking. This system acquires sole pressure distribution changes via a mat type load distribution sensor, and does personal authentication. We employ twelve features based on the shape of a footprint, and twenty seven features based on weight movement for sole pressure data. Fuzzy if-then rules for each registered person are introduced, within which, their parameters are statistically determined in the learning process. We calculate the fuzzy degree of a pair of right and left sole pressure data for any registered person, and identify the walking person as the person with the highest fuzzy degree; the fuzzy degree being higher than a threshold. We employed 90 volunteers and authenticated them. We evaluate the proposed fuzzy method by five hold cross validation on which low false rejection and false acceptance rates are achieved. Thus, this fuzzy logic approach is precise for this biometric system.