Biometric Authentication Methods Research Articles

Multichannel Acoustic Spectroscopy of the Human Body for Inviolable Biometric Authentication.

Specific features of the human body, such as fingerprint, iris, and face, are extensively used in biometric authentication. Conversely, the internal structure and material features of the body have not been explored extensively in biometrics. Bioacoustics technology is suitable for extracting information about the internal structure and biological and material characteristics of the human body. Herein, we report a biometric authentication method that enables multichannel bioacoustic signal acquisition with a systematic approach to study the effects of selectively distilled frequency features, increasing the number of sensing channels with respect to multiple fingers. The accuracy of identity recognition according to the number of sensing channels and the number of selectively chosen frequency features was evaluated using exhaustive combination searches and forward-feature selection. The technique was applied to test the accuracy of machine learning classification using 5,232 datasets from 54 subjects. By optimizing the scanning frequency and sensing channels, our method achieved an accuracy of 99.62%, which is comparable to existing biometric methods. Overall, the proposed biometric method not only provides an unbreakable, inviolable biometric but also can be applied anywhere in the body and can substantially broaden the use of biometrics by enabling continuous identity recognition on various body parts for biometric identity authentication.

Meta-Heuristic Optimization and Keystroke Dynamics for Authentication of Smartphone Users

Personal Identification Numbers (PIN) and unlock patterns are two of the most often used smartphone authentication mechanisms. Because PINs have just four or six characters, they are subject to shoulder-surfing attacks and are not as secure as other authentication techniques. Biometric authentication methods, such as fingerprint, face, or iris, are now being studied in a variety of ways. The security of such biometric authentication is based on PIN-based authentication as a backup when the maximum defined number of authentication failures is surpassed during the authentication process. Keystroke-dynamics-based authentication has been studied to circumvent this limitation, in which users were categorized by evaluating their typing patterns as they input their PIN. A broad variety of approaches have been proposed to improve the capacity of PIN entry systems to discriminate between normal and abnormal users based on a user’s typing pattern. To improve the accuracy of user discrimination using keystroke dynamics, we propose a novel approach for improving the parameters of a Bidirectional Recurrent Neural Network (BRNN) used in classifying users’ keystrokes. The proposed approach is based on a significant modification to the Dipper Throated Optimization (DTO) algorithm by employing three search leaders to improve the exploration process of the optimization algorithm. To assess the effectiveness of the proposed approach, two datasets containing keystroke dynamics were included in the conducted experiments. In addition, we propose a feature selection algorithm for selecting the proper features that enable better user classification. The proposed algorithms are compared to other optimization methods in the literature, and the results showed the superiority of the proposed algorithms. Moreover, a statistical analysis is performed to measure the stability and significance of the proposed methods, and the results confirmed the expected findings. The best classification accuracy achieved by the proposed optimized BRNN is 99.02% and 99.32% for the two datasets.

A Patch-Based CNN Built on the VGG-16 Architecture for Real-Time Facial Liveness Detection

Facial recognition is a prevalent method for biometric authentication that is utilized in a variety of software applications. This technique is susceptible to spoofing attacks, in which an imposter gains access to a system by presenting the image of a legitimate user to the sensor, hence increasing the risks to social security. Consequently, facial liveness detection has become an essential step in the authentication process prior to granting access to users. In this study, we developed a patch-based convolutional neural network (CNN) with a deep component for facial liveness detection for security enhancement, which was based on the VGG-16 architecture. The approach was tested using two datasets: REPLAY-ATTACK and CASIA-FASD. According to the results, our approach produced the best results for the CASIA-FASD dataset, with reduced HTER and EER scores of 0.71% and 0.67%, respectively. The proposed approach also produced consistent results for the REPLAY-ATTACK dataset while maintaining balanced and low HTER and EER values of 1.52% and 0.30%, respectively. By adopting the suggested enhanced liveness detection, architecture that is based on artificial intelligence could make current biometric-based security systems more secure and sustainable while also reducing the risks to social security.

Covered Style Mining via Generative Adversarial Networks for Face Anti-spoofing

Face anti-spoofing, a biometric authentication method, is a central part of automatic face recognition. Recently, two sets of approaches have performed particularly well against presentation attacks: 1) pixel-wise supervision-based methods, which intend to provide fine-grained pixel information to learn specific auxiliary maps; and 2) anomaly detection-based methods, which regard face anti-spoofing as an open-set training task and learn spoof detectors using only bona fide data, where the detectors are shown to generalize well to unknown attacks. However, these approaches depend on handcrafted prior information to control the generation of intermediate difference maps and easily fall into local optima. In this paper, we propose a novel frame-level face anti-spoofing method, Covered Style Mining-GAN (CSM-GAN), which converts face anti-spoofing detection into a style transfer process without any prior information. Specifically, CSM-GAN has four main components: the Covered Style Encoder (CSE), responsible for mining the difference map containing the photography style and discriminative clues; the Auxiliary Style Classifier (ASC), consisting of several stacked Difference Capture Blocks (DCB) responsible for distinguishing bona fide faces from spoofing faces; and the Style Transfer Generator (STG) and Style Adversarial Discriminator (SAD), which form generative adversarial networks to achieve style transfer. Comprehensive experiments on several benchmark datasets show that the proposed method not only outperforms current state-of-the-art but also produces better visual diversity in difference maps.

Liveness Detection Based on Improved Convolutional Neural Network for Face Recognition Security

—Face liveness detection is an important biometric authentication method for face recognition securitythat is used to determine a fake face from an authentic one. In this paper, a liveness detection method based on optimized LeNet5 is proposed. The LeNet-5 is optimized by increasing the convolution kerneland byintroducing a global average pooling. The simulation results show that the proposed model obtained the highest recognition rate of 99.95% as against the 96.67% and 98.23% accuracy from the Support Vector Machine (SVM) and LeNet-5 models, respectively.The results denote that the proposed model has a high recognition rate in face liveness detection

One-Shot Face Recognition through a Region Inception ResNet with Modified Triplet Loss

Face recognition has become a convenient method for identity verification, especially the one-shot task has much practical value. Some former works have achieved considerable results. However, they perform ill under the situation of COVID-19 with more and more people wearing a mask. This requires the extracted face features distinguishable and robust enough for classifying different masked people. It is difficult for the one-shot task with only one sample for training. To solve this problem, we designed a network called Region Inception ResNet with Modified Triplet Loss, which generates robust features. It keeps high accuracy even under masked condition. The networks are trained on the datasets of CASIA-Webface and Ms-Celeb-1M, tested on LFW (Labelled Faces in the Wild). Experiments in Section 6 show the effectiveness of our method.

An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.

Forearm multimodal recognition based on IAHP‐entropy weight combination

Biometrics are the among most popular authentication methods due to their advantages over traditional methods, such as higher security, better accuracy and more convenience. The recent COVID-19 pandemic has led to the wide use of face masks, which greatly affects the traditional face recognition technology. The pandemic has also increased the focus on hygienic and contactless identity verification methods. The forearm is a new biometric that contains discriminative information. In this paper, we proposed a multimodal recognition method that combines the veins and geometry of a forearm. Five features are extracted from a forearm Near-Infrared (Near-Infrared) image: SURF, local line structures, global graph representations, forearm width feature and forearm boundary feature. These features are matched individually and then fused at the score level based on the Improved Analytic Hierarchy Process-entropy weight combination. Comprehensive experiments were carried out to evaluate the proposed recognition method and the fusion rule. The matching results showed that the proposed method can achieve a satisfactory performance.

Masked face recognition: Human versus machine

The recent COVID-19 pandemic has increased the focus on hygienic and contactless identity verification methods. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition (FR) in a collaborative environment is a currently sensitive yet understudied issue. Recent reports have tackled this by evaluating the masked probe effect on the performance of automatic FR solutions. However, such solutions can fail in certain processes, leading to the verification task being performed by a human expert. This work provides a joint evaluation and in-depth analyses of the face verification performance of human experts in comparison to state-of-the-art automatic FR solutions. This involves an extensive evaluation by human experts and 4 automatic recognition solutions. The study concludes with a set of take-home messages on different aspects of the correlation between the verification behaviour of humans and machines.

Biometric authentication utilizing fuzzy extractor with PSO based security ensuring the data security mechanism as Trio in Cloud

Cloud computing is widely preferred by various organizations due to its numerous advantages such as lower costs, better security, and faster processing times. The most critical data stored in the remote servers are protected by firewalls and encryption, at the same time, the user’s essential data may be accessed from the remote cloud server without their control. This kind of data storage facility introduces various security complications. In order to prevent these issues, we have proposed a method that uses a Biometric authentication method with PSO to provide high-level security. The method combines a pseudo-integer authentication scheme with a secret OTP that can be used to authenticate an individual. A Tri-level security mechanism model is proposed to store three types of data classification namely, No Privacy, Privacy with Trusted Provider and Privacy with Non-PNR. The proposed method provides a uniform security scheme for all the classifications using Adaptive ElGamal with Menezes-Qu-Verstone, that prevents data loss and corruption caused by insiders in the cloud. Further Seed Block is utilized to recover and extract data without loss. It does so by effectively extracting the data from the formatted disks. It is then followed with the decryption process, as if the data are requested by the user. Our framework aims to provide a secure and authenticated environment for accessing data. It features an improved authentication process and reduced complexity.

Bilateral Ear Acoustic Authentication: A Biometric Authentication System Using Both Ears and a Special Earphone

In existing biometric authentication methods, the user must perform an authentication operation such as placing a finger in a scanner or facing a camera. With ear acoustic authentication, the acoustic characteristics of the ear canal can be used as biometric information. Therefore, a person wearing earphones does not need to perform any authentication operation. Existing studies which use the acoustic characteristics of the ear canal as biometric information only measure the characteristics of one ear. However, the acoustic characteristics of the human ear canal can be measured from both ears. Hence, we proposed a new method for acoustic authentication based on the ability to measure the acoustic characteristics of the ear canal from both ears. The acoustic characteristics of the ear canal of 52 subjects were measured. Comparing the acoustic characteristics of the left and right ear canals, a difference in the signal between the left and right ear was observed. To evaluate the authentication accuracy, we calculated the evaluation indices of biometric authentication, equal error rate (EER), and area under curve (AUC). The EER for bilateral ear acoustic authentication using signals from both ears was 0.39%, which was lower than that of a single ear. The AUC was 0.0016 higher for bilateral ear acoustic authentication. Therefore, the use of bilateral signals for ear acoustic authentication was proved to be effective in improving authentication accuracy.

Managing the New Wave of Migration With Biometric Identification

Due to the geopolitical situation, the scarcity of raw materials, the foreign policy actions of the major powers, the excessive use of resources worldwide and the struggle for them, as well as the inequalities caused by the demographic explosion of developing countries, europe and thus Hungary from the wider Middle East region have been under considerable migratory pressure for a long time, and this has increased further in 2021 on the basis of statistical data. (Frontex). Europe is trying to provide more answers to the problem. It is trying to keep refugees as far away as possible by developing and improving living conditions in the countries where migration flows originate, as well as by establishing migration zones beyond the EU’s borders and financing them, because these problems need to start to be addressed and solved at local level. Despite all this, refugees are constantly reaching Europe, and unfortunately among them are not only those who fear for their lives and lose everything, fleeing war, but also economic migrants who join the wave of migration and are probably members of organisations that pose a threat to European values and public security. (Kotzur, Moya, Sözen, Romano 2020). By building physical border closures, several EU countries have closed certain migration routes, but this has not eliminated the problem, only the routes have been reorthled. The registration and clear identification of immigrants in the attempt to cross the border and their identification within the EU pose a number of unresolved problems for the authorities. (Beňuška, T., Nečas, 2021). We are reviewing and comparing the methods of identification used in border policing with modern biometric identification methods in order to draw attention to the fact that technology has long been ready to be used to protect our security in the service of European values and security. But at the same time, a significant deterrent is the difficult transition from entrenched solutions to the fear of misuse of biometric data. In order to achieve this, it is necessary to consider trade-offs in use and technology and to think more effectively together at EU level as a common solution on the regulatory side. (EU Regulation 399/2016).

The Identification of ECG Signals Using WT-UKF and IPSO-SVM.

The biometric identification method is a current research hotspot in the pattern recognition field. Due to the advantages of electrocardiogram (ECG) signals, which are difficult to replicate and easy to obtain, ECG-based identity identification has become a new direction in biometric recognition research. In order to improve the accuracy of ECG signal identification, this paper proposes an ECG identification method based on a multi-scale wavelet transform combined with the unscented Kalman filter (WT-UKF) algorithm and the improved particle swarm optimization-support vector machine (IPSO-SVM). First, the WT-UKF algorithm can effectively eliminate the noise components and preserve the characteristics of ECG signals when denoising the ECG data. Then, the wavelet positioning method is used to detect the feature points of the denoised signals, and the obtained feature points are combined with multiple feature vectors to characterize the ECG signals, thus reducing the data dimension in identity identification. Finally, SVM is used for ECG signal identification, and the improved particle swarm optimization (IPSO) algorithm is used for parameter optimization in SVM. According to the analysis of simulation experiments, compared with the traditional WT denoising, the WT-UKF method proposed in this paper improves the accuracy of feature point detection and increases the final recognition rate by 1.5%. The highest recognition accuracy of a single individual in the entire ECG identification system achieves 100%, and the average recognition accuracy can reach 95.17%.

RP-Net: A PointNet++ 3D Face Recognition Algorithm Integrating RoPS Local Descriptor

as a biometric identification method in the post-epidemic era, face recognition owing more and more attention in practical applications to its non-contact and interaction-friendly advantages. Researchers more favor 3D faces because they have richer spatial information than 2D faces and are not easily affected by the environment. However 3D faces are not all collected in normal environments. To enhance the facial features of 3D faces and improve the recognition degree of 3D faces in weak-light or dark environments, a 3D face recognition algorithm based on point cloud depth learning is proposed. First, 3D faces are automatically detected from 3D raw data and preprocessed, including nose-tip detection and face cropping, spike removal and hole filling, and surface normals. Then, rotated projection statistical local feature descriptors (RoPS) are integrated into the PointNet++ network to describe and classify local features. Finally, feature matching is performed using the nearest neighbor distance ratio. The algorithm was tested on the Bosphorus and CASIA-3D datasets, and good results were obtained in a simulated weak-light environment.

Biometric Identification based on the Multidimensional Transient Functions of the Human Oculo-Motor System

Recently, identification systems that use human biometric characteristics in solving the problem of access to information systems are becoming more common. The paper proposes a new method of biometric identification of users of computer systems based on the definition of integral Volterra models of the human oculomotor system (OMS) according to experimental research “input-output” using innovative technology of eye tracking. Developed in the Python IDLE programming environment software to identify OMS. Experimental studies of the OMS of two individuals were performed. Based on the data obtained using the TOBII PRO TX300 eye tracker, the transient functions of the first, second and third orders of the OMS are determined. There is a significant difference in the diagonal intersections of the transition functions of the second and third orders of the two individuals.

DEVELOPMENT OF A FINGERPRINT PATTERN MATCHING METHOD USING K-MEANS

Biometric identification methods wstudied. Biometric identification of fingerprints and methods of their use are analyzed. As a recognition, fingerprint-based intelligent analysis identification is preferred, in particular the matching of a scanned fingerprint to a template. The paper analyzed the main filtering algorithms of the K-Means clustering method. The the study result of the program operation with the presented method of filtering the fingerprints were obtained. The developed identification system is based on Arduino Nano in combination with a DY50 fingerprint scanner. The software of this system is implemented using the C++ language and integrated using the Arduino IDE. Keywords : filtering, Ateb-Gabor filtering, biometric images.

Application of facial recognition technology to prevent insider attacks

The article discusses the types of biometric authentication to prevent insider attacks, their comparative analysis. There are various methods of determining insider activity. Today, Data Leak Prevention (DLP) systems, which are used to prevent the leakage of confidential information from the information system, are widespread. However, these software complexes do not allow detecting a potential attacker at an early stage, as well as monitoring the connection of users, since a tight connection is formed between users and computers with access rights of their level. Biometric innovations are a solution that allows providing a sufficient level of security, user convenience and optimization of business processes. Among the main types of biometric authentication methods, static methods of human biometrics have become the most widespread. These include identification based on the papillary pattern on the fingers, the iris and retina of the eye, the pattern of hand veins, the geometry of the hands, and the face. The family of methods using dynamic characteristics include: voice identification, handwriting dynamics, heart rate, and gait. Biometric technologies consist in measuring and using unique physical or behavioral traits of people to distinguish them from each other. Biometric recognition forms a strong connection between a person and his personality as a biometric feature. It cannot be separated from a person, lost or duplicated. So, biometric recognition is a protection method more resistant to social engineering attacks compared to conservative recognition methods (passwords, tokens, etc.).Also the approach to identification of human faces and description of the system of their recognition working in a mode close to real time which allows to trace a subject’s head, and then distinguishes it, comparing initial features of a face with earlier recognized is offered.

Building a classifier in the personality recognition system based on eye tracking data

Identification systems that use biometric characteristics to solve the problem of access to information systems are becoming more common. The article proposes a new method of biometric identification of computer systems users, based on the determination of the integral Volterra model of the human oculo-motor system (OMS) according to experimental research "input-output" using innovative eye tracking technology. With the help of the Tobii Pro TX300 eye tracker, the data of OMC responses to test visual stimuli were obtained, displayed as bright dots on the computer screen at different distances from the start position in the "horizontal" direction. Based on the data obtained, the transition functions of the first, second and third orders of the OMS for two people were determined. To construct a personality classifier, the informativeness of the proposed heuristic features, determined on the basis of the transition functions in terms of the probability of correct recognition (PCR), is investigated. Pairs of features are established that are resistant to computational errors and have a high PCR value - in the range 0.92 - 0.97. Fig.: 8. Table: 5. Bibliography: 30 items.
 Key words: biometric identification, personality recognition, Volterra model, oculo-motor system, eye tracking technology, informativeness of features, classification.

Leakage or Identification

The convenience of laptops brings with it the risk of information leakage, and conventional security systems based on the password or the explicit biometric do little to alleviate this problem. Biometric identification based on anatomical features provides far stronger security; however, a lack of suitable sensors on laptops limits the applicability of this technology. In this paper, we developed a behavior-irrelevant user identification system applicable to laptops with a metal casing. The proposed scheme, referred to as LeakPrint, is based on leakage current, wherein the system uses an earphone to capture current leaking through the body and then transmits the corresponding signal to a server for identification. The user identification is achieved via denoising, dimension reduction, and feature extraction. Compared to other biometric identification methods, the proposed system is less dependent on external hardware and more robust to environmental noise. The experiments in real-world environments demonstrated that LeakPrint can verify user identity with high accuracy (93.6%), while providing effective defense against replay attacks (96.5%) and mimicry attacks (90.9%).

Identity verification using palm print microscopic images based on median robust extended local binary pattern features and k-nearest neighbor classifier.

Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods. However, this biometrics domain also has several critical challenges: image rotation, image displacement, change in image scaling, presence of noise in the image due to devices, region of interest (ROI) detection, or user error. For this purpose, a new method of identity verification based on median robust extended local binary pattern (MRELBP) is introduced in this study. In this system, after normalizing the images and extracting the ROI from the microscopic input image, the images enter the feature extraction step with the MRELBP algorithm. Next, these features are reduced by the dimensionality reduction step, and finally, feature vectors are classified using the k-nearest neighbor classifier. The microscopic images used in this study were selected from IITD and CASIA data sets, and the identity verification rate for these two data sets without challenge was 97.2% and 96.6%, respectively. In addition, computed detection rates have been broadly stable against changes such as salt-and-pepper noise up to 0.16, rotation up to 5°, displacement up to 6 pixels, and scale change up to 94%.