Biometric Database Research Articles

Biometric access system with automatic registration and loyalty check in the system of recognition and database entry

ABSTRACT Against the backdrop of ongoing terrorist threats and uncontrolled migration flows, there is increased interest in biometric human recognition technology and barcoding technology combined with it. As a possible solution, a biometric access system is proposed, designed to implement three main urgent tasks – automatic registration of people crossing the border; recording registration information in a special biometric database; checking registration information and facial image on the database of wanted people. Effective biometric systems combine verification and identification tasks to increase the reliability of person identification and comparison with reference data sets. In the course of the study, various methods of information processing with its subsequent encoding were considered and analyzed, and the experience of previous systems of this type was investigated. As a result of the study, the effectiveness of using the latest biometric technologies in the migration sphere and in law enforcement agencies for the effective search of offenders has been substantiated. This paper will be useful as a material for further study of facial recognition technology and the integration of QR into the field of biometrics.

Who are they? A retrospective study of unidentified bodies in Institute of Medical-Legal Paris from 2018 to 2023.

Despite great advances in human identification, unidentified decedents remain a global problem.This 6-year overview study covering a total of 2 324 unidentified bodies admitted to the IMLP provided relevant information about the unidentified decedent population and assessed the impact of a protocol established in 2017 on the rate of deceased buried without identity in Paris.The need to establish a national database in France to properly document and disseminate information on missing persons and to centralize the biological profile of unidentified bodies is key, as without antemortem information or a biometric database there can be no matching.

Biometric Systems: A Comprehensive Review

As the utilization of biometric data grows, it is expected that systems will become more dependable and capable of providing successful outcomes in challenging scenarios and in the face of counterfeiting. Ensuring the security of information is important to the effective functioning of any system. Hence, a stringent approach is necessary to authenticate the identity of each individual prior to granting them access to the stored data. Cancellable biometrics can enhance the security and privacy of users in response to potential threats. This study reviews and discusses many components of the biometric system, including face recognition, fingerprint identification, iris recognition, speaker recognition, gait recognition, ear recognition, and hand scanning. Authentication for the Biometric database is included in each section. For each segment, we will evaluate an article and analyze the discoveries presented in that paper during our review. Authentication is performed by all sections inside a filed biometric database

Enhancing Recognition in Multimodal Biometric Systems: Score Normalization and Fusion of Online Signatures and Fingerprints

Multimodal biometrics employs multiple modalities within a single system to address the limitations of unimodal systems, such as incomplete data acquisition or deliberate fraud, while enhancing recognition accuracy. This study explores score normalization and its impact on system performance. To fuse scores effectively, prior normalization is necessary, followed by a weighted sum fusion technique that aligns impostor and genuine scores within a common range. Experiments conducted on three biometric databases demonstrate the promising efficacy of the proposed approach, particularly when combined with Empirical Modal Decomposition (EMD). The fusion system exhibits strong performance, with the best outcome achieved by merging the online signature and fingerprint modalities, resulting in a normalized Min-Max score-based Equal Error Rate (EER) of 1.69%.

A multimodal biometric database and case study for face recognition based deep learning

Recently, multimodal biometric systems have garnered a lot of interest for the identification of human identity. The accessibility of the database is one of the contributing elements that impact biometric recognition systems. In their studies, the majority of researchers concentrate on unimodal databases. There was a need to compile a fresh, realistic multimodal biometric database, nonetheless, because there were so few comparable multimodal biometric databases that were publically accessible. This study introduces the MULBv1 multimodal biometric database, which contains homologous biometric traits. The MULBv1 database includes 20 images of each person's face in various poses, facial emotions, and accessories, 20 images of their right hand from various angles, and 20 images of their right iris from various lighting positions. The database contains real multimodal data from 174 people, and all biometrics were accurately collected using the micro camera of the iPhone 14 Pro Max. A face recognition technique is also suggested as a case study using the gathered facial features. In the case study, the deep convolutional neural network (CNN) was used, and the findings were positive. Through several trials, the accuracy was (97.41%).

Preliminary Experiments of a Real-World Authentication Mechanism Based on Facial Recognition and Fully Homomorphic Encryption

In the current context in which user authentication is the first line of defense against emerging attacks and can be considered a defining element of any security infrastructure, the need to adopt alternative, non-invasive, contactless, and scalable authentication mechanisms is mandatory. This paper presents initial research on the design, implementation, and evaluation of a multi-factor authentication mechanism that combines facial recognition with a fully homomorphic encryption algorithm. The goal is to minimize the risk of unauthorized access and uphold user confidentiality and integrity. The proposed device is implemented on the latest version of the Raspberry Pi and Arduino ESP 32 modules, which are wirelessly connected to the computer system. Additionally, a comprehensive evaluation, utilizing various statistical parameters, demonstrates the performance, the limitations of the encryption algorithms proposed to secure the biometric database, and also the security implications over the system resources. The research results illustrate that the Brakerski–Gentry–Vaikuntanathan algorithm can achieve higher performance and efficiency when compared to the Brakerski–Fan–Vercauteren algorithm, and proved to be the best alternative for the designed mechanism because it effectively enhances the level of security in computer systems, showing promise for deployment and seamless integration into real-world scenarios of network architectures.

Biometric systems for identification and verification scenarios using spatial footsteps components

AbstractHumans are distinguished by their walking patterns; many approaches, including using various types of sensors, have been used to establish walking patterns as biometrics. By studying the distinguishing features of a person's footsteps, footstep recognition may be utilized in numerous security applications, such as managing access in protected locations or giving an additional layer of biometric verification for secure admittance into restricted regions. We proposed biometric systems for verifying and identifying a person by acquiring spatial foot pressure images from the values obtained from the piezoelectric sensors using the Swansea Foot Biometric Database, which contains 19,980 footstep signals from 127 users and is the most prominent open-source gait database available for footstep recognition. The images acquired are fed into the ConvNeXt model, which was trained using the transfer learning technique, using 16 stride footstep signals in each batch with an Adam optimizer and a learning rate of 0.0001, and using sparse categorical cross-entropy as the loss function. The proposed ConvNeXt model has been adjusted to acquire 512 feature vectors per image, and these feature vectors are used to train the logistic regression models. We propose two biometric systems. The first biometric system is based on training one logistic regression model as a classifier to identify 40 different users using 1600 signals for training, 6697 signals for validation, and 200 signals for evaluation. The second biometric system is based on training 40 logistic regression models, one for each user, to validate the user's authenticity, with a total number of 2363 training signals, 7077 validation signals, and 500 evaluation signals. Each of the 40 models has a 40-training signal per client and a different number of signals per imposter, a different number of signals for the validation that ranges between 8 and 650 signals, a 5-signal for an authenticated client, and a different number of signals per imposter for evaluation. Independent validation and evaluation sets are used to evaluate our systems. In the biometric identification system, we obtained an equal error rate of 15.30% and 21.72% for the validation and evaluation sets, while in the biometric verification system, we obtained an equal error rate of 6.97% and 10.25% for the validation and evaluation sets.

Multi-Modal Biometric Recognition for Face and Iris using Gradient Neural Network (Gen-NN)

In recent years, Biometric system are the one, which is widely used method for the recognition and identification of an individual that are highly demanded approach for its absolute security and accuracy which plays a vital roles in banking, commercials, business and other fields. Moreover this research is based on the multimodal biometrics which is recommended for its high recognition performances and it overcome the demerits of unimodal biometric approach. This research concentrate two multi-modal biometric traits such as face and iris, and propose Gradient Neural Network (Gen-NN) method to improve the biometric authentication by using the VISA face and iris multi-modal biometric database also used ResNet-101 and WaveNet for the feature extraction where the input of face and iris can be extracted.

Benford’s Law and Perceptual Features for Face Image Quality Assessment

The rapid growth in multimedia, storage systems, and digital computers has resulted in huge repositories of multimedia content and large image datasets in recent years. For instance, biometric databases, which can be used to identify individuals based on fingerprints, facial features, or iris patterns, have gained a lot of attention both from academia and industry. Specifically, face image quality assessment (FIQA) has become a very important part of face recognition systems, since the performance of such systems strongly depends on the quality of input data, such as blur, focus, compression, pose, or illumination. The main contribution of this paper is an analysis of Benford’s law-inspired first digit distribution and perceptual features for FIQA. To be more specific, I investigate the first digit distributions in different domains, such as wavelet or singular values, as quality-aware features for FIQA. My analysis revealed that first digit distributions with perceptual features are able to reach a high performance in the task of FIQA.

Robust biometric scheme against replay attacks using one-time biometric templates

User authentication is an important issue on the Internet and usually solved through static and often unique passwords. Another method is to use biometrics, but biometric data are sensitive and need to be protected. Protection schemes such as cancelable biometric template generation have appeared, but they are sensitive to replay attacks. In this paper, we propose an original method to generate one-time biometric templates for user authentication applications. This proposed scheme limits replay attacks, consisting of an attacker maliciously retransmitting an intercepted user's identity proof. Our method is generic: any biometric modality could be used, the identity verification is realized by the service/identity provider to be realistic. Biometric features are extracted from captures using deep learning and then protected with biohashing, a cancelable biometric scheme. Finally, a step consisting of cryptographic hashing and symmetric encryption guarantees the generation of a one-time, non-replayable template. We have tested our scheme on two common biometric databases, from faces and fingerprints, and the results confirm its efficiency and robustness to attacks given a rigorous threat model.

Automatic Signature Verifier Using Gaussian Gated Recurrent Unit Neural Network

Handwritten signatures are one of the most extensively utilized biometrics used for authentication, and forgeries of this behavioral biometric are quite widespread. Biometric databases are also difficult to access for training purposes due to privacy issues. The efficiency of automated authentication systems has been severely harmed as a result of this. Verification of static handwritten signatures with high efficiency remains an open research problem to date. This paper proposes an innovative introselect median filter for preprocessing and a novel Gaussian gated recurrent unit neural network (2GRUNN) as a classifier for designing an automatic verifier for handwritten signatures. The proposed classifier has achieved an FPR of 1.82 and an FNR of 3.03. The efficacy of the proposed method has been compared with the various existing neural network-based verifiers.

A comparative analysis of multi-biometrics performance in human and action recognition using silhouette thermal-face and skeletal data

Biometrics is a field that has been given importance in recent years and has been extensively studied. Biometrics can use physical and behavioural differences that are unique to individuals to recognize and identify them. Today, biometric information is used in many areas such as computer vision systems, entrance systems, security and recognition.In this study, a new biometrics database containing silhouette, thermal face and skeletal data based on the distance between the joints was created to be used in behavioural and physical biometrics studies. The fact that many cameras were used in previous studies increases both the processing intensity and the material cost. This study aimed to both increase the recognition performance and reduce material costs by adding thermal face data in addition to soft and behavioural biometrics with the optimum camera. The presented data set was created in accordance with both motion recognition and person identification.Various data loss scenarios and multi-biometrics approaches based on data fusion have been tried on the created data sets and the results have been given comparatively. In addition, the correlation coefficient of the motion frames method to obtain energy images from silhouette data was tested on this dataset and yielded high-accuracy results for both motion and person recognition.

Can Sleep Apnea Be Detected from Human Pulse Waveform with Laplace Noise?

Differential privacy is a powerful technique that protects the privacy of individuals in a dataset by adding controlled randomness. With the increasing developments in smart sensors, the use of various biometric database is expanding. If privacy protections coexist with advanced use of the biometric database, wider utilization is expected. One of the promising approaches is to apply differential privacy to biometric information, which is attracting attention in use cases such as Google. By adding Laplace noise to biometric information, differential privacy can be added. Our aim is to focus on peak to peak interval of electrocardiogram. It is useful bio-information because it is possible to know not only heart disease but also various physical conditions such as exercise amount, activity amount, fatigue, sleep based on it. In this study, we demonstrated that differential privacy can be applied to obtain the sleep apnea index from PPIs with Laplace noise. The observed correlations were 0.96 to 0.99 for the corresponding PPIs with Laplace noise.

Multi random projection inner product encryption, applications to proximity searchable encryption for the iris biometric

Biometric databases collect people's information and perform proximity search (finding records within bounded distance of the query) with few cryptographic protections. This work studies proximity searchable encryption applied to the iris biometric.Prior work proposed to build proximity search from inner product functional encryption (Kim et al., SCN 2018). This work identifies and closes two gaps in this approach:1.Biometrics use long vectors, often with thousands of bits. Many inner product encryption schemes have to invert a matrix whose dimension scales with this size. Setup is then not feasible on commodity hardware. We introduce a technique that improves setup efficiency without harming accuracy.2.Prior approaches leak distance between queries and all stored records. We propose a construction from function hiding, predicate, inner product encryption (Shen et al., TCC 2009) that avoids this leakage.Finally, we show that our scheme can be instantiated using symmetric pairing groups, which improves search efficiency.

Multimodal Biometric System Based on the Fusion in Score of Fingerprint and Online Handwritten Signature

Abstract Multimodal biometrics is the technique of using multiple modalities on a single system. This allows us to overcome the limitations of unimodal systems, such as the inability to acquire data from certain individuals or intentional fraud, while improving recognition performance. In this paper, a study of score normalization and its impact on the performance of the system is performed. The fusion of scores requires prior normalisation before applying a weighted sum fusion that separates impostor and genuine scores into a common interval with close ranges. The experiments were carried out on three biometric databases. The results show that the proposed strategy performs very encouragingly, especially in combination with Empirical Modal Decomposition (EMD). The proposed fusion system shows good performance. The best result is obtained by merging the globality online signature and fingerprint where an EER of 1.69 % is obtained by normalizing the scores according to the Min-Max method.

A Deep Learning Technique for Biometric Authentication Using ECG Beat Template Matching

An electrocardiogram (ECG) is a unique representation of a person’s identity, similar to fingerprints, and its rhythm and shape are completely different from person to person. Cloning and tampering with ECG-based biometric systems are very difficult. So, ECG signals have been used successfully in a number of biometric recognition applications where security is a top priority. The major challenges in the existing literature are (i) the noise components in the signals, (ii) the inability to automatically extract the feature set, and (iii) the performance of the system. This paper suggests a beat-based template matching deep learning (DL) technique to solve problems with traditional techniques. ECG beat denoising, R-peak detection, and segmentation are done in the pre-processing stage of this proposed methodology. These noise-free ECG beats are converted into gray-scale images and applied to the proposed deep-learning technique. A customized activation function is also developed in this work for faster convergence of the deep learning network. The proposed network can extract features automatically from the input data. The network performance is tested with a publicly available ECGID biometric database, and the proposed method is compared with the existing literature. The comparison shows that the proposed modified Siamese network authenticated biometrics have an accuracy of 99.85%, a sensitivity of 99.30%, a specificity of 99.85%, and a positive predictivity of 99.76%. The experimental results show that the proposed method works better than the state-of-the-art techniques.

A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices.

In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of the system. For this, a strict biometric evaluation protocol has been followed, using a biometric database composed of data acquired from 38 subjects by means of a commercial smartwatch in two different sessions. A cross-session test (where training and testing data were acquired in different days) was performed. Following the state of the art, the proposal was tested with different configurations in the acquisition, pre-processing, feature extraction and classification stages, achieving improvements in all of the scenarios; improvements of 100% (0% error) were even reached in some cases. This shows the advantages of including the proposed technique, whatever the system.

Epidermal politics: Control, violence and dissent at the biometric border

In this paper, we critically interrogate the registration of migrants in pan-European, large-scale biometric databases, like Eurodac (European Asylum Dactyloscopy Database). We employ the notion of “epidermal politics”, which analytically captures how human bodies – and skin in particular – become sites of identification, violent control, and contestation. Thinking through epidermal politics allows us to understand how the development of technologies that render skin visible and analysable, such as fingerprint scanners and biometric matching algorithms, are entangled in relations of power, structural racism, and subjugation. Drawing on the work of Simone Browne (2015) and her elaboration of Franz Fanon’s theory of epidermisation, we argue that migration control in Europe, and its violent and racialising effects, are embedded within data infrastructures that “stigmatise” (Van Der Ploeg 1999) post-colonial “others” with codes to control their mobilities. We unpack this argument in three stages. First, we discuss the governmental rationales that inform the use of Eurodac for the management of migration and asylum in Europe. Second, we discuss how biometric control is related to different forms of state violence, including deportation, prolonged detention, and physical violence associated with the forced registration of migrants’ fingerprints. Third, we attend to strategies employed by migrants to contest biometric control, focusing specifically on fingertip burning and mutilation, which we interpret as acts of dissent and self-determination to escape control. Overall, our goal is to emphasise the need to pay closer attention to dynamics of violence and racialisation that emerge at biometric and other kinds of “hi-tech” borders.

Heartprint: A Dataset of Multisession ECG Signal with Long Interval Captured from Fingers for Biometric Recognition

The electrocardiogram (ECG) signal produced by the human heart is an emerging biometric modality that can play an important role in the future generation’s identity recognition with the support of machine learning techniques. One of the major obstacles in the progress of this modality is the lack of public datasets with a long interval between sessions of data acquisition to verify the uniqueness and permanence of the biometric signature of the heart of a subject. To address this issue, we put forward Heartprint, a large biometric database of multisession ECG signals comprising 1539 records captured from the fingers of 199 healthy subjects. The capturing time for each record was 15 s, and recordings were made in resting and reading conditions. They were collected in multiple sessions over ten years, and the average interval between first session (S1) and third session (S3L) was 1572.2 days. The dataset also covers several demographic classes such as genders, ethnicities, and age groups. The combination of raw ECG signals and demographic information turns the Heartprint dataset, which is made publicly available online, into a valuable resource for the development and evaluation of biometric recognition algorithms.

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes.