Biometric Authentication System Research Articles

FIMBISAE: A Multimodal Biometric Secured Data Access Framework for Internet of Medical Things Ecosystem

Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.

Current Livestock Identification Systems

Traditional livestock identification and tracking techniques have been used for a while, but their performance is constrained by their susceptibility to theft, fraud, and security issues. This review discusses bioactive identifiers, biometric identifiers, barcodes, radio frequency identification and others as current means of identifying livestock and the potential benefits of using biometric identification systems such as improved accuracy and efficiency, as well as their ability to provide more reliable data than traditional identification methods. We also discussed the challenges and concerns associated with the use of biometric identification systems. Selecting the best method from the classical approaches involves various considerations, such as the size of the farm, the cost, and the required functions of the identification process. The article concludes by discussing the future of biometric identification systems in the livestock industry. We recommend that farmers should consider these factors in choosing the best animal identification system for their farms

An investigation of IRIS Recognition Techniques: A Literature Survey

Iris is a colored muscle located within the eye that helps to regulate the amount of light that reaches the eye. It has some specific textural details that is not easily altered or manipulated, making it a characteristic that is ideally suited to biometric systems. Iris has been considered in the field of biometric applications for authentication purpose. The main purpose of this study is, due to the stability and unique feature available in Iris region. Iris patterns play a major role in many possible identification or authentication applications due to their uniqueness, durability and stability, universality. Iris recognition techniques have evolved tremendously in biometric identification and authentication systems over the past two decades, since their evolution. The technique of Iris recognition is quantifiable, robust, and highly accurate so that it achieves the fundamental tenant of ideal biometric technology. Purpose of this paper is to include a timeline analysis of different iris recognition techniques and the development of a novel algorithm for the iris recognition system. This paper also discusses various methods used in performing the iris recognition measures involved.

Low-Cost and Portable Smartphone-Assisted Indonesian Electronic Identity (e-KTP) Verification System

Low-Cost and Portable Smartphone-Assisted Indonesian Electronic Identity (e-KTP) Verification System

Verified play, precarious work: GamerGate and platformed authenticity in the cultural industries

This article argues that GamerGate, a critical hashtag event in the history of digital harassment, is key to understanding contemporary identity verification systems and digital labour. We build our argument from a comparative analysis of two case studies: (1) digital journalistic responses to GamerGate and (2) Twitter’s account verification ‘checkmark’ system from 2021 to 2022. These phenomena showcase the linkages between the gendered and raced policing of journalists and users during GamerGate and the rise of ‘authenticity’ as a key resource for journalists and other platformed creators in the present. We draw on digital games, journalism and critical media studies to analyse the work of ‘authenticity’. We argue that platform affordances such as identity verification badges are fundamentally implicated in the work of users to appear ‘real’, even as the visibility requisite for realness brings uneven risks for marginalised cultural workers.

Biometric Identification Systems with Both Chosen and Generated Secret Keys by Allowing Correlation

We propose a biometric identification system where the chosen- and generated-secret keys are used simultaneously, and investigate its fundamental limits from information theoretic perspectives. The system consists of two phases: enrollment and identification phases. In the enrollment phase, for each user, the encoder uses a secret key, which is chosen independently, and the biometric identifier to generate another secret key and a helper data. In the identification phase, observing the biometric sequence of the identified user, the decoder estimates index, chosen- and generated-secret keys of the identified user based on the helper data stored in the system database. In this study, the capacity region of such system is characterized. In the problem settings, we allow chosen- and generated-secret keys to be correlated. As a result, by permitting the correlation of the two secret keys, the sum rate of the identification, chosen- and generated-secret key rates can achieve a larger value compared to the case where the keys do not correlate. Moreover, the minimum amount of the storage rate changes in accordance with both the identification and chosen-secret key rates, but that of the privacy-leakage rate depends only on the identification rate.

A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques

Identity management describes a problem by providing the authorized owners with safe and simple access to information and solutions for specific identification processes. The shortcomings of the unimodal systems have been addressed by the introduction of multimodal biometric systems. The use of multimodal systems has increased the biometric system’s overall recognition rate. A new degree of fusion, known as an intelligent Dual Multimodal Biometric Authentication Scheme, is established in this study. In the proposed work, two multimodal biometric systems are developed by combining three unimodal biometric systems. ECG, sclera, and fingerprint are the unimodal systems selected for this work. The sequential model biometric system is developed using a decision-level fusion based on WOA-ANN. The parallel model biometric system is developed using a score-level fusion based on SSA-DBN. The biometric authentication performs preprocessing, feature extraction, matching, and scoring for each unimodal system. On each biometric attribute, matching scores and individual accuracy are cyphered independently. A matcher performance-based fusion procedure is demonstrated for the three biometric qualities because the matchers on these three traits produce varying values. The two-level fusion technique (score and feature) is implemented separately, and their results with the current scheme are compared to exhibit the optimum model. The suggested plan makes use of the highest TPR, FPR, and accuracy rates.

Information about computer handwriting: The challenges of finding a balance between individual liberty and state security

The present study is devoted to the introduction of biometric identification systems in law enforcement. The author pays special attention to the phenomenon of computer handwriting, examining in detail the constitutional and legal aspects of the use of knowledge about it. For this purpose a wide methodological apparatus is used: comparative-legal method, method of analogy, deduction, method of modelling and others. The Russian practice and potential for further development are compared with the experience of foreign countries both in the regulation of general issues of biometric personal data and in situations of legal regulation of computer handwriting data. The author investigates how to simultaneously solve the problems of detection and investigation of offences in which printed texts act as evidentiary materials, and at the same time preserve human rights to privacy, personal and family secrecy, anonymity on the Internet. It is noted that computer handwriting is a rather young phenomenon for legal science; there is still no in-depth study of it from the perspective of this field of knowledge. This paper is only a beginning in understanding the topic under consideration. It raises basic questions: whether it is permissible to use knowledge about computer handwriting, how it should be protected and where it should be stored, whether it is necessary to limit the existing commercial interest in its use. As a result, the author comes to the following conclusions: it is permissible and necessary to collect information on computer handwriting, as this will reduce infringements on the natural rights of man and citizen. For such collection it is necessary to use a centralised state system in which all the requirements for the security of critical information infrastructure are implemented. However, before such a system is put into operation, it is necessary to support commercial companies in relevant developments and oblige them to provide information about users' computer handwriting at the request of authorised persons.

DM-EEGID: EEG-Based Biometric Authentication System Using Hybrid Attention-Based LSTM and MLP Algorithm

DM-EEGID: EEG-Based Biometric Authentication System Using Hybrid Attention-Based LSTM and MLP Algorithm

Face Recognition Based Biometric Identification System with COVID-19 Detection

Abstract: In this paper face recognition system for the purpose of security is explored whose design is based on Raspberry Pi system. An add on temperature detection system also gives detection of COVID affected people. With the COVID-19 pandemic, temperature checking before you enter a premise is a necessary step to ensure you are well and help keep others safe. One of the most common ways is to use a handheld digital thermometer to find out your own temperature and recording it on some kind of logbook placed near the front door. For a workplace, employees are encouraged to record their temperature twice a day

Secure personal authentication in fog devices via multimodal rank‐level fusion

SummaryIn recent years, Fog and edge computing have gained enormous increases and usage of Internet of Things (IoT) devices in research and development even though it provides challenges such as security and privacy. One way to ensure this is a fusion of multimodal biometrics authentication systems, which offer highly reliable biometric authentication solutions to reduce the failure to enroll rate, increase the degree of freedom, and deter spoof attacks. Since unimodal biometric systems often face significant limitations due to sensitivity to noise, data quality, non‐universality, and illumination variations. In this research, we propose management of access control to ensure the desired level of security, a Rank level fusion integration method. ORL, GT, CASIA, and VGG‐face databases were used for testing and evaluating the implemented system. Thus, using the LDA with SCE based feature selection approach, the average GAR percent for the facial, hearing, and palm vein is 3.25% for the facial and muffs pulse and 8.55% for the head and palm vein result was the overall performance of the biometric system even in the presence of low‐quality data in the Fog environment.

Prohibited artificial intelligence practices in the proposed EU artificial intelligence act (AIA)

Artificial intelligence (AI) now forms a more and more important part of human lives. After years focussed on the development of AI, the initial hype about its many expected benefits has gradually given way to rising ethical concerns about its inherent risks and dangers. Efforts to confront and contain the most serious risks related to AI have now prompted a number of legislative or regulatory proposals at the national, regional and global level. One of the most comprehensive regulatory initiatives is the European Union's proposal for an Artificial Intelligence Act (AIA), which was released in April 2021 with a view towards establishing a legal framework for trustworthy AI. To this end, the draft AIA pursues a proportionate horizontal and risk-based regulatory approach to AI, broadly classifying AI into the categories of unacceptable risks, high risks and low or minimal risks. The unacceptable risks are those that are deemed to contravene European Union values, and therefore, they are considered to be ‘prohibited AI practices’ by Article 5 AIA. The prohibited AI practices are classified into four categories, namely 1) AI systems deploying subliminal techniques; 2) AI practices exploiting vulnerabilities; 3) social scoring systems; and 4) ‘real-time’ remote biometric identification systems. The proposed regulatory approach, however, appears problematic given the four categories’ inherent interrelatedness and the numerous possibilities for their mutual combination and entwinement. It is also problematic from the perspective of the human mind, as each of the four categories alone allows for the manipulation of human thought and behaviour, thereby endangering freedom of thought and other fundamental rights. In the context of the proposed AIA, both aspects give rise to unknown and unsolved conundrums that create difficult regulatory challenges that raise the necessity to also look at the wider implications of these technologies for the entire legal system. As these conundrums often find their expression in paradoxes and oxymora, this article calls for a wider interdisciplinary debate and advocates a different regulatory strategy using these concepts to transcend the limitations inherent in dualistic or dichotomous modes of legal thinking.

Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions

Biometrics has been evolving as an exciting yet challenging area in the last decade. Though face recognition is one of the most promising biometrics techniques, it is vulnerable to spoofing threats. Many researchers focus on face liveness detection to protect biometric authentication systems from spoofing attacks with printed photos, video replays, etc. As a result, it is critical to investigate the current research concerning face liveness detection, to address whether recent advancements can give solutions to mitigate the rising challenges. This research performed a systematic review using the PRISMA approach by exploring the most relevant electronic databases. The article selection process follows preset inclusion and exclusion criteria. The conceptual analysis examines the data retrieved from the selected papers. To the author, this is one of the foremost systematic literature reviews dedicated to face-liveness detection that evaluates existing academic material published in the last decade. The research discusses face spoofing attacks, various feature extraction strategies, and Artificial Intelligence approaches in face liveness detection. Artificial intelligence-based methods, including Machine Learning and Deep Learning algorithms used for face liveness detection, have been discussed in the research. New research areas such as Explainable Artificial Intelligence, Federated Learning, Transfer learning, and Meta-Learning in face liveness detection, are also considered. A list of datasets, evaluation metrics, challenges, and future directions are discussed. Despite the recent and substantial achievements in this field, the challenges make the research in face liveness detection fascinating.

Practical Consideration in using Pre-trained Convolutional Neural Network (CNN) for Finger Vein Biometric

Using a pre-trained Convolutional Neural Network (CNN) model for a practical biometric authentication system requires specific procedures for training and performance evaluation. There are two criteria for a practical biometric system studied in this paper. First, the system’s ability to handle identity theft or impersonation attacks. Second, the ability of the system to generate high authentication performance with minimal enrollment period. We propose the use of the Multiple Clip Contrast Limited Adaptive Histogram Equalization (MC-CLAHE) technique to process finger images before being trained by CNN. A pre-trained CNN model called AlexNet is used to extract features as well as classify the MC-CLAHE images. The authentication performance of the pre-trained AlexNet model has increased by a maximum of 30% when using this technique. To ensure that the pre-trained AlexNet model is evaluated based on its ability to prevent impersonation attacks, a procedure to generate the Receiver Operating Characteristics (ROC) curve is proposed. An offline procedure for training the pre-trained AlexNet model is also proposed in this paper. The purpose is to minimize the user enrollment period without compromising the authentication performance. In this paper, this procedure successfully reduces the enrollment time by up to 95% compared to using on-line training.

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).

PRNU Anonymous Algorithm Used for Privacy Protection in Biometric Authentication Systems

The photo response non-uniformity (PRNU) is used to connect an image to its source sensor. In this paper, researchers propose a PRNU anonymity method based on image segmentation to cut the relationship between the image and its source camera. According to the distribution rule of PRNU in the high and low frequency band of the image, the high and low frequency information of the part is also processed differently, which ensures the quality of the output image to a large extent. Experiments on the datasets show that the proposed method can preserve the biometric characteristics of the device while maintaining the anonymity of the device. Comparing with prior art, peak signal to noise ratio (PSNR) and cosine similarity are improved by 1.9 dB and 0.02 points, respectively.

Cancelable scheme for bimodal biometric authentication

The use of a biometric authentication system (BAS) for reliable automatic human recognition has increased exponentially over traditional authentication systems in recent years. Multimodal BAS was introduced to solve unimodal BAS’s difficulties and improve security. Privacy and security are two significant concerns to be addressed in BAS, as biometric traits are irrevocable. Researchers employed cancelable biometrics in the past few years to propose several privacy-preserving BAS. We propose a privacy-preserving bimodal cancelable BAS (PPBCBAS) to overcome these problems. The traits used in our method are iris and fingerprint. Features are extracted from both the traits, and feature level fusion is done by concatenating the feature vectors of iris and fingerprint. PPBCBAS uses a quotient filter to generate the cancelable template, and the comparison is made on these transformed templates using the modified Hamming distance. PPBCBAS has been tested on three publicly available databases to analyze its efficiency. PPBCBAS satisfies the diversity, irreversibility, and revocability properties and achieves decent performance.

CNN-Based Transfer Learning for 3D Knuckle Recognition

A trustworthy and secure identity verification system is in great demand nowadays. The automatic recognition of the 3D middle finger knuckle is a new biometric identifier that could offer a precise, practical, and efficient alternative for personal identification. According to earlier studies, deep learning algorithms could be used for biometric identification. However, the accuracy of the current 3D middle finger knuckle recognition model is relatively low. Motivated by this fact, in this study, seven deep learning neural networks have been modified and trained to identify 3D middle finger knuckle patterns using transfer learning. Using the Hong Kong Polytechnic University’s 3D knuckle image dataset, an extensive experiment was performed. Two sessions of data from different camera lenses were used to assess the performance of the suggested deep learning model. The results show that the InceptionV3 method significantly enhanced the recognition of 3D middle finger knuckle patterns with 99.07% accuracy, followed by Xception, NasNetMobile, and DenseNet201 (97.35%, 92.92%, and 92.59%, respectively), which is superior to the current middle finger knuckle recognition model. This accurate, fast, and automatic middle finger knuckle identification will help to be implemented in real-time and small-scale settings like offices, schools, or personal devices like laptops and smartphones, where training is simple.

Facial Image Encryption for Secure Face Recognition System

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks.

Touchscreen gestures as images. A transfer learning approach for soft biometric traits recognition

Mobile devices are nowadays ubiquitous. They are equipped with a variety of sensors, each designed for capturing specific signals which can be exploited to acquire discriminating user’s traits, thus allowing to recognize, for instance, the authorized users. In this regard, we focus on capturing soft biometric traits from smartphones. Soft biometric information extracted from a human body (e.g., gender and age) is ancillary information proved to improve the performance of biometric authentication systems, and it has drawn a great deal of attention for its applications in healthcare, smart spaces and digital world as a whole. This paper presents an approach to gender and age-group recognition, namely TGSB, leveraging a transfer learning strategy applied to state-of-the-art Convolutional Neural Networks (CNNs) fed with image-based representations of touch gestures performed by users on mobile devices. We perform experiments considering, one at a time, touch gestures of the same kind, and combinations thereof, with intermediate and late fusion learning strategies. Experiments prove that TGSB is a promising approach, with up to 94% accuracy for the gender recognition and up to 99% for the age-group recognition. We highlight the most useful touch gesture for gender and age-group recognition, that is Scroll with 81% and 96% accuracy, respectively. We show that combining multiple touch gestures (intermediate fusion) with a joint latent subspace learning mechanism in CNNs improves the TGSB performance, up to 99% accuracy when considering a combination of two Scroll. Compared to previous works, TGSB exhibits much better performance in both gender and age-group recognition.