Secure cloud-based biometric authentication utilising smart devices for electronic transactions

Biometric cryptosystems are widely studied recently, which bind a cryptographic key with the biometric characteristic of a user in such a way that the key cannot be recovered without a successful biometric authentication. By now, the key is generated from extra random number generators, which makes the system more complex. A novel method is proposed, which generates the key from biometric characteristics directly, and makes the biometric cryptosystems much cheaper, simpler and more convenient. To make the keys secure enough, the TD-ERCS is adopted and revised in the proposed algorithm. Experiments show that the algorithm is sensitive to biometric characteristics, free of collision, and the generated keys seem also random. It is believed that such a algorithm can be practically used for various biometric cryptosystems.

The article presents biometric systems from three different standpoints. Technological standpoint, where the focus is laid on biometric system usage by the general public. Explaining basic terms and difficulties using various biometric characteristics. It also shortly describes how recognition works in several biometric characteristics (fingerprint, face, iris, and signature). After that is the legal standpoint, which is focused mainly on European Union law, where the often-mentioned GDPR is discussed, this basic legal regulation places a significantly higher standard than the previous legislation on the processing of biometric data as a particular category of personal data. Lastly, the article shows a sociological standpoint. In that part, different attitudes towards biometric technologies are discussed within the world population and different groups of the Czech Republic population. In the latest survey done by the authors in 2020 was found that age, and education play a vital role in the knowledge about biometric systems.

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.

Multimodal Biometrics is an emerging domain in biometric technology where more than one biometric trait is combined to improve the performance. The biometric system take Face, Fingerprint, Voice, Handwritten Signatures, Retina, Iris, Gait, Palm print, Ear & Hand geometry as common features. Human is identified by correct matching of these features. However, features like face, voice, and signature have low permanence and they change with time. Ageing of human, as well as other psychological & environmental conditions cause gradual change in these features. While enrolling feature set we don't consider this factor. Here we propose a new concept that can be used in designing future multimodal biometrics systems which can adapt to the change in the biometrics features like face, voice, signature, and gait over the time or any other factor without compromising the security. Regression based technique can be used to detect change. This algorithm requires use of at least one biometric feature which has very low variance or high degree of permanence, like Fingerprint, Iris, Retina etc. This algorithm can address the problem of false rejection caused by sustained change in biometric features due to Ageing or any other factor without the need of re-enrollment of feature set.

Traditional authentication methods use numbers or graphic passwords and thus involve the risk of loss or theft. Various studies are underway regarding biometric authentication because it uses the unique biometric data of a human being. Biometric authentication technology using ECG from biometric data involves signals that record electrical stimuli from the heart. It is difficult to manipulate and is advantageous in that it enables unrestrained measurements from sensors that are attached to the skin. This study is on biometric authentication methods using the neural network with weighted fuzzy membership functions (NEWFM). In the biometric authentication process, normalization and the ensemble average is applied during preprocessing, characteristics are extracted using Haar-wavelets, and a registration process called “training” is performed in the fuzzy neural network. In the experiment, biometric authentication was performed on 73 subjects in the Physionet Database. 10-40 ECG waveforms were tested for use in the registration process, and 15 ECG waveforms were deemed the appropriate number for registering ECG waveforms. 1 ECG waveforms were used during the authentication stage to conduct the biometric authentication test. Upon testing the proposed biometric authentication method based on 73 subjects from the Physionet Database, the TAR was 98.32% and FAR was 5.84%.

<p>Biometrics associated with automated methods of identifying a person or verifying the identity of a person based on physiological or behavioral characteristics. Commonly used biometric features are facial features, fingerprints, voice, facial thermo grams, iris, posture/gait, palm print, hand geometry etc. Compared with other biometric characteristics iris is the most stable and hence the most reliable biometric characteristic over the period of a lifetime. This proposed work provides comparative study of various filters of Wavelet Transforms in terms of size and PSNR of images<em>.</em></p>

Continuous authentication is a promising method for protecting computer systems in the event of compromise of primary authentication factors, such as passwords or tokens. Systems employing continuous authentication that rely on biometrics may not be restricted to a single biometric characteristic; rather, they can simultaneously utilize multiple characteristics and subsequently arrive at a conclusive decision based on their collective analysis outcomes. One of the significant challenges researchers encounter when investigating effective fusion in decision-making is the lack of data. At present, data generation primarily involves the creation of feature vectors or attack simulation. This paper introduces a method for directly generating distances derived from a Siamese neural network, utilizing the probability density function of an existing distribution. Through statistical analysis, we successfully generated 5000 samples that correspond to the initial distribution, which were then employed to discover the threshold values at which FAR and FRR were less than 1%. The methods developed can be further applied to identify the most efficient strategies for integrating the results of continuous authentication in systems that incorporate multiple biometric characteristics.

Biometric characteristics allow identifying an individual based on who they are. This paper focuses on using the of a subject in electronic identity documents. The International Civil Aviation Organization (ICAO) has endorsed the adoption of the as the globally interoperable biometric characteristic. Soon afterward, the International Standard Organization (ISO) proposed the ISO/IEC 19794-5 standard in accordance with ICAO directives. In this paper, a new approach for the evaluation of three ISO/ICAO requirements (unnatural skin tone, across the and flash reflection on skin) is presented. The results achieved by the proposed methods have overcome almost all the works in the literature. For the shadows across the face requirement, the proposed method obtained a result 41% better than the best result in the literature. The proposed method is able to keep the rejection rate equal to zero even when it does not achieve the best equal error rate.

The aim of this work is to develop an authentication method using keyboard typing based on fuzzy logic. The paper considers a function that takes into account the minimum and maximum limits of biometric characteristics and the dependence of the stability of keyboard typing on its stability. As a result, a deviation factor is introduced to determine how much the biometric characteristic of the user after normalization will vary from the average value, which in its turn allows to determine the percentage of authenticity of the computer information system user. As a result, the probability of occurrence of errors of the first and second kind is reduced in comparison with the existing methods of authentication.

Animal biometrics based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features, and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This study presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. The proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust features and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum‐rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of 93.87% identification accuracy. The comparative analysis of experimental results for proposed work and appearance‐based face recognition algorithms has been done at each level.

Objective: The objective of this study was to examine of manner quantitatively how biometric technologies can improve financial interoperability in the metaverse. The impact of facial recognition and biometric authentication on security perceptions, user experience, and usage intent in virtual environments was analyzed, as well as the relationship between security perceptions and users' willingness to adopt these technologies in their financial transactions. Materials and methods: A quantitative method was used with a non-experimental, cross-sectional, and descriptive design. Surveys were applied to 150 participants, including financial technology experts and active users of the metaverse. The key variables, such as perception of security, ease of use, and adoption intention, were measured using Likert-type scales and analyzed using descriptive and inferential statistics, including correlation coefficients and reliability analysis through of the alpha of Cronbach's. Results: The results showed a significant positive correlation (r = 0.78; p < 0.001) between the perception of security and the intention to use these technologies. 92% of participants signaled improved security as their primary benefit, and 90% highlighted faster transaction times. The scales used were highly reliable (α > 0.87), demonstrating the consistency of the instruments employed. Conclusions: It is conclusion is that biometric technologies are an effective solution for improving financial interoperability in the metaverse, as a positive perception of security significantly increases adoption intention. Furthermore, the need to establish regulations and ethical standards to guarantee data privacy is highlighted. Implementing these technologies in financial institutions would not only optimize their services but also strengthen user trust and engagement in digital environments.

Human resource management in defense is crucial for the efficiency, security, and readiness of military organizations. Emerging biometric recognition technologies and their application in defense HR management, facilitated by artificial intelligence (AI), play a pivotal role in advancing biometric identification systems. These systems incorporate technologies such as fingerprint recognition, facial recognition, iris scanning, and voice identification. When combined with AI-driven algorithms, they enhance the accuracy and efficiency of biometric data analysis and processing. This paper examines the mathematical algorithms underpinning the identification process, including support vector machines (SVM), deep neural networks (DNN), and biometric feature extraction methods. Special emphasis is placed on the challenges associated with implementing such technologies in the defense sector, encompassing ethical and legal considerations related to personal data protection.

In recent years, development in non-invasive and painless medical imaging such as Computed Tomography (CT) or MRI (magnetic resonance imaging), has improved the process of diseases diagnosis and clarification, including tumours, cysts, injuries, and cancers. The full-body scanner with superior spatial resolution provides essential details of complicated anatomical structures for effective diagnostics. However, it is challenging for a physician in glance over a large data-set of over hundreds or even thousands of images (2D “slices” of the body). Consider a case when a doctor wants to view a patient’s CT or MRI scans for analysing, he needs to review and compare among many layers of 2D image stacks (many 2D slices make a 3D stack). If the patient is scanned multiple time (three consecutive months, for instance) to confirm the growth of the tumours, the dataset is turned to be 4D (time-stamp added). The manual analysing process is time-consuming, troublesome and labour-intensive. The innovation of Augmented Reality (AR) in the last few decades allows us to illuminate this problem. In this paper, we propose an AR technique which assists the doctor in instantly accessing and viewing a patient’s set of medical images quickly and easily. The doctor can use an optical head-mounted display such as the Google Glass, or a VR Headset such as the Samsung Gear VR, or a general smartphone such as the Apple iPhone X. He looks at one palm-sized AR Tag with patient’s document embedded with a QR code, and the smart device could detect and download the patient’s data using the decrypted QR code and display layers of CT or MRI images right on top of the AR tag. Looking in and out from the tag allows the doctor to see the above or below of the current viewing layer. Moreover, shifting the looking orientation left or right allows the doctor to see the same layer of images but in different timestamp (e.g. previous or next monthly scans). Our obtained results demonstrated that this technique enhances the diagnosing process, save cost and time for medical practice.

Using an extended technology acceptance model to investigate facial authentication

Biometric Authentication (BA) has been recently identified as a significant paradigm on maintaining the security level to improve individual person's authenticity. With more and more advanced technology, the BA has to be enhanced to cope up with the increased insecure environment. Several schemes employing multimodal biometric system fuses the face image with certain other traditional biometric modalities to ensure security. However changes in the facial expression significantly updates the facial geometry, thereby reduces the ranking score level. Some of the multimodal biometric research group has designed different algorithms on personal biometric feature based authentication. But, fusion multimodal biometric system did not provide robustness compromising the security level. To develop high robust multimodal biometric system, person multimodal biometric using Covariance Matrix Incremental Principal Component Analysis (CMI-PCA) method is proposed in this paper. The main goal of CMI-PCA method is to work with three principle steps and attain rank level fusion integration on the faces, ears and hand dorsal vein. First, the person's multimodal biometric features are sensed and feature extraction is performed using Gabor Filter based Incremental PCA to improve robustness level. Second step is to match the extracted features with stored test image from the database using Score Matching based on Covariance Matrix Incremental PCA. The score matching based on Covariance Matrix Incremental PCA maintain the scale of extracted features and compute the mean score value to match with test images without any Covariance Matrix range. Finally, the CMI-PCA method involves combining different biometric identification ranks for making final decision. The extended Borda Count Multimodal Ranking system is used in CMI-PCA to determine the integrated biometric outcome and ensures higher security level for individual information. Experiment is conducted on factors such as robustness level, multimodal matching score rate, rank level fusion efficiency rate.