Design of a Vein Based Personal Identification System

A finger vein authentication system is proposed in this research. Biometrics is the science of determining a person's identity based on physiological or behavioral characteristics. Physical characteristics like fingerprints, a face or a retina, as well as personal characteristics like a signature, are included in these characteristics. Biometric features are significantly more difficult for attackers to replicate or fabricate than traditional methods, and they are extremely rare to lose. Biometric traits are used in the identification system, which increases security and dependability. The technology to verify vein patterns is still relatively new, compared with other human characteristics. The proposed work focuses on developing a contactless sensor to retrieve features from the hand's finger vein pattern using a Deep attribute Representation based Fractional Firefly method (DAR-FFF). Vein pattern identification scans the blood for hemoglobin using an infrared light source. After the participant's palm is placed over the sensing device, an infrared region beam from the device measures the orientation of the arteries. These ultraviolet wavelengths are absorbed by liquid hemoglobin in the vasculature, resulting in dark streaks on the map. The hand's finger has more intricate circulatory pathways and a variety of distinguishing characteristics. Image enhancement, skeletonization, and vein pattern chain code comparison are all processes in this procedure.

Finger vein pattern recognition using image processing technique

Many biometrics, such as face, fingerprint and iris images, have been studied extensively for personal verification purposes in the past few decades. However, verification using vein patterns is less developed compared to other human traits. A new personal verification system using the thermal-imaged vein pattern in the back of the hand is presented in the paper. The system consists of five individual steps:Data Acquisition, Image Enhancement, Vein Pattern Segmentation, Skeletonization and Matching. Unlike most biometric systems that carry out comparisons based on a pre-selected feature set, this system directly recognizes the shapes of the vein pattern by measuring their Line-Segment Hausdorff Distance. Preliminary testing on a database containing 108 different images has been carried out and all the images are correctly recognized.

A sole biometric identifier in construction an individual identification is often not able to meet the preferred performance requirements. Biometric identification pedestal on multiple biometrics correspond to an up-and-coming trend. computerized biometric scheme for person identification gauge a “signature” of the human body, compare the resulting characteristic to a database, and cause to be an request dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are classically characteristic, even though point in time varying, such as Face recognition, Iris recognition, Fingerprint verification, Palm print verification in making a personal identification. Multi-biometric systems, which consolidate in sequence from multiple biometric sources, are in advance popularity since they are able to conquer limitations such as non-universality, noisy sensor data, large intra-user difference and vulnerability to spoof attacks that are commonly encountered in uni-biometric systems. In this paper, it addresses the concept issues and the applications strategies of multi-biometric systems.

A biometric system is an amalgamation of algorithms that uses an individual’s unique biological data innate physical features (fingerprints, retina, face, ears, etc.) or behavioural features (signature, voice, keystroke dynamics, etc.) or both to authenticate the individual. This essentially makes a biometric system function as a pattern recognition system that helps an individual in any kind of adversarial environment. Hence, like any conventional authentication system, it is vulnerable to the attacks of malicious adversaries that try to manipulate the data and thus decline the security of the system by compromising its robustness. These possible attacks have not been considered while developing most biometric authentication systems. Our work describes multimodal biometrics, its feature extraction methods, research challenges and focuses on the major security issues in both unimodal and multimodal biometric systems. This paper summarizes some robust multimodal systems, their countermeasures and algorithms that have been devised to tackle these security challenges.KeywordsBiometricsMultimodal biometricsBiometric fusionSecurityAttacksCountermeasuresEncryption

User authentication is a fundamental component of cybersecurity. Traditional password-based systems have long been the standard for verifying identity in digital environments, but increasing threats such as phishing, brute-force attacks, and password reuse have exposed their vulnerabilities. Biometric authentication using unique physiological or behavioural characteristics like fingerprints, facial recognition, or iris scans has emerged as a promising alternative. This paper presents a comparative study between password and biometric authentication systems focusing on their security, usability, reliability, and performance. A data-driven analysis using secondary data and user-based experiments highlights the differences in accuracy, authentication time, and user satisfaction. Results show that biometric systems outperform password authentication in terms of security and user convenience but face challenges related to privacy, cost, and environmental reliability. The paper concludes that hybrid models combining biometrics and passwords can balance usability and security for modern applications.

There is an ever-increasing need for an efficient, and accurate vein detection system that can work in clinical scenarios. Traditional methods, while effective, grapple with challenges like high error rates and susceptibility to environmental variations, making the need for advanced solutions more pressing. This study addresses these limitations by unveiling a groundbreaking Convolutional Neural Network (CNN) architecture, tailored specifically for the nuanced recognition of vein patterns, a critical component in biomedical systems. The existing frameworks in vein pattern recognition often falter in handling intricate variations, leading to diminished accuracy and reliability levels. The proposed model revolutionizes this landscape by integrating a modified Densenet-161 structure for analyzing blood flow velocity and recognizing dorsal hand vein locations. The ingenuity lies in the removal of the conventional classification layer, transforming it into a robust feature embedder. This strategic alteration enables the extraction of remarkably distinct vein features from hand images, a leap forward in capturing the unique identifiers crucial in biomedical systems. The methodology employed is twofold: a meticulous training and validation phase, followed by a testing phase under different verification scenarios. The training harnesses the power of the CNN feature embedder, extracting and classifying features with an unprecedented precision. The testing phase introduces a dual input system, comparing known and unknown vein patterns using Euclidean distance, a method that quantifies similarity with remarkable accuracy levels. This process significantly reduces intra-class variations while amplifying inter-class differences, culminating in a system that is not only highly accurate but also reliable for clinical scenarios. The model's superiority is further evidenced by its performance on various datasets, showcasing improvements in precision, accuracy, recall, AUC, and reduced delay, outperforming existing methods by notable margins. The model can thus be used for intravenous catheterization, blood check-up, and analysis of different veins identified related to different diseases.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Nowadays, biometric recognition represents an integral component of identity management and access control systems, replacing PINs or passwords. However, the wide deployment of biometric recognition systems in the past decades has raised numerous privacy concerns regarding the storage and use of biometric data. Due to the fact that the link between individuals and their biometric characteristics, e.g. fingerprints or iris, is strong and permanent, biometric reference data (templates) need to be protected in order to safeguard individuals' privacy and biometric systems' security. In particular, unprotected biometric templates can be abused to crossmatch biometric databases, i.e. tracking individuals without consent, and to launch presentation attacks employing specific inversion techniques. Technologies of biometric template protection offer solutions to privacy preserving biometric authentication, which improves the public confidence and acceptance of biometric systems. While biometric template protection has been an active research topic over the last 20 years, proposed solutions are still far from gaining practical acceptance. The existing gap between theory and practice, which results in a trade-off between privacy protection and recognition accuracy, is caused by numerous factors. To facilitate a standardized design and application of biometric template protection schemes, researchers have to solve several open problems. This chapter provides an overview of state-of-the-art technologies of biometric template protection and discusses main issues and challenges.

Biometric systems such as fingerprint, iris, DNA became popular methods in user authentication. Compared to these biometric systems, keystroke biometric authentication systems have not gained so much attention because of lower accuracy compared to other biometric systems. A number of researches have been conducted on keystroke biometric using different generative and discriminative classifiers. As Hidden Markov Models have proven a great success in voice recognition, this study investigates Hidden Markov Models in keystroke dynamic. This paper proposes a novel user verification technique using 1-substate Hidden Markov Model through keystroke dynamic. To verify the effectiveness of the proposed system, extensive experiments have been conducted and 80% accuracy was achieved by the proposed system.

Recently, finger vein sensors have been embedded in all kinds of electronic devices for personal identification, such as intelligent door locks and attendance machines. The embedded sensors are generally small, thus capturing only part of the finger vein. However, prior studies have focused on near-full finger vein recognition, without considering the partial finger vein image caused by the small imaging window of the finger vein sensor. This paper aims to study personal identification based on partial finger vein images, known as small-area finger vein recognition. The effect of the small-area finger vein on recognition performance is first analyzed by cutting out the local part from the near-full finger vein image to model a small-area finger vein image. Second, a small-area finger vein database is built using a commercial finger vein imaging device, in which the vein pattern from approximately one-third of one adult finger is captured. To explore more discriminative information from small-area finger vein images, we propose a locality-constrained consistent dictionary learning (LCDL) method to fuse multiple features for small-area finger vein recognition. Finally, the proposed method is evaluated on the self-built small-area finger vein database and four synthetic small-area finger vein databases. Experimental results show the promising recognition performance of the proposed method.

Biometrics are the unique characteristics of the individual that differentiate him or her from any other person. Down and Sands [1] explained that the physiological characteristics refer to the inherited traits that are shaped in the early embryonic stages of the human development. Physical biometrics include, among other things, DNA, fingerprints, hand geometry, vein patterns, face structure, skin luminescence, palm prints, iris patterns, periocular features, retina patterns, ear shape, lip prints, heartbeats, tongue prints, and body odor/scent [2]?[8]. Behavioral characteristics are not inherited but acquired and learned throughout the life of the individual [1]. These include, but also are not limited to, signature, handwriting, vocal prints, keystroke dynamics, and gait?body motion [3]. As a result, the biometrics of a person cannot be stolen, forgotten, or forged. It is what we are [2].

Biometrics is the important area of distinguishing people using their behavioural characteristics. Until now, researcher and exporter increasing interest with vein pattern biometrics. A vein pattern is a massive link of blood vessels under a person's skin. Similar to fingerprints, in scientific sense, the shape of vascular patterns in the same part of the body has proved distinct from each other. The objective of this paper is to analyse vein images and to design and implement dorsal hand-vein recognition system that has the ability to segment vein and recognise each person based on his vein requires the presence of the human operator. The experimental results indicate that the MDC classifier achieves accuracy of 92% in the case of wavelet transform, and GLCM and Tamura features.

Traditional authentication methods such as passwords are susceptible to easy hacking, and as technology progresses, the demand is on the rise for more reliable and secure recognition systems, which can be used in surveillance and biometrics applications etc. A biometric recognition system of individuals is established on the unique features of the individual. Multi modal biometric systems represent a significant research field with widespread applications of recognition systems. Unimodal biometric systems have a variety of issues, including nonuniversality and noisy data. Certain limitations and difficulties can be partially addressed with the use of multi modal biometric systems. In this paper, a survey on multi-biometric systems is presented to highlight the challenges, strengths and weaknesses of some of the research discussed in this study which contributes to enhancing the understanding and development of robust and reliable biometric authentication solutions, essential for ensuring security in various domains as well as suggest directions for future work. Index Terms— biometric recognition, multimodal biometric, biometric system, fusion techniques, multi-biometric system.

Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.