Image Analysis and Coding Based on Ordinal Data Representation

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 Most student identification cards outdated personal information.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.

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. An approach for accurate Biometric Recognition and Identification of Human Iris Patterns using Neural Network has been illustrated by gopikrishnan et. al. It has been concluded by Yingzi Du et. al that the partial iris portion of the iris pattern describes the uniqueness and the pupil has no direct effect on the accuracy of the biometric recognition. In this paper the Iris recognition has been carried out employing a template of size 10 × 480 pixels instead of 20 × 480 pixels as employed in the earlier paper. The results of the two sizes of the templates have been compared and it has been observed that the accuracy of the results obtained with the limited template size is comparable with that of the one with the full size. The reason for this is also discussed in this paper. The improved methodology suggested has resulted in the reduction of the space requirement as well as time complexity with no loss in accuracy. This paper also provides results of iris recognition performed applying Hamming distance, Feed forward back propagation, Cascade forward back propagation, Elman forward back propagation and perceptron. It has been established that the method suggested applying perceptron provides the best accuracy in respect of iris recognition with no major additional computational complexity.

Traditional biometric systems are designed and configured to operate in predefined circumstances to address the needs of a particular application. The performance of such biometrics systems tend to decrease because when they encounter varying conditions as they are unable to adapt to such variations. Many real-life scenarios require identification systems to recognise uncooperative people in uncontrolled environments. Therefore, there is a real need to design biometric systems that are aware of their context and be able to adapt to changing conditions. The context-awareness and adaptation of a biometric system are based on a set of factors that include: the application (e.g. healthcare system, border control, unlock smart devices), environment (e.g. quiet/noisy, indoor/outdoor), desired and pre-defined requirements (e.g. speed, usability, reliability, accuracy, robustness to high/low quality samples), user of the system (e.g. cooperative or non-cooperative), the chosen modality (e.g. face, speech, gesture signature), and used techniques (e.g. pre-processing to normalise and clean biometrics data, feature extraction and classification). These factors are linked and might affect each other, hence the system has to work adaptively to meet its overall aim based to its operational context. The aim of this research is to develop a multiagent based framework to represent a context-aware adaptive biometric system. This is to improve the decision making process at each processing step of traditional biometric identification systems. Agents will be used to provide the system with intelligence, adaptation, flexibility, automation, and reliability during the identification process. The framework will accommodate at least five agents, one for each of the five main processing steps of a typical biometric system (i.e. data capture, pre-processing, feature extraction, classification and decision). Each agent can contribute differently towards its designated goal to achieve the best possible solution by selecting/ applying the best technique. For example, an agent can be used to assess the quality of the input biometric sample to ensure the important features can be extracted and processed in further steps. Another agent can be used to pre-process the biometric sample if necessary. A third agent is used to select the appropriate set of features followed by another to select a suitable classifier that works well in a given condition.

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.

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.

Friction ridge impression from the areas of the fingers or hands that are not from the fingertips are called non-distal prints, which include palm prints, middle and proximal phalangeal prints, and interphalangeal prints. The presence of these fingerprints is less compared to distal print however, once retrieved from the scene of crime allow the identification of the individual who committed the crime. Therefore, this case report presents a burglary case where phalangeal prints from the finger retrieved from the site of crime matched the prints collected from the suspect. This case report also goes through advancements brought by Automated Biometric Identification System (ABIS) such as enhancing low-quality latent fingerprint, real-time picture and matching, and integration of other biometric systems such as Iris scan, Facial recognition.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

Access control is the process of protecting resources by allowing or rejecting access to particular resources. It is an integral part of physical security systems and protects resources from unauthorized use. Over the past decade, there has been a huge demand and need for more sophisticated and secure access technologies. Scientists are developing cutting edge technologies to fulfill the requirements for secure access control experiences. Access control systems, using the latest biometric technologies such as fingerprint, facial recognition, iris recognition, DNA, and signature recognition have offered a higher level of security than the conventional systems. Biometrics plays a vital role in the identification and authentication phase of access control. Every biometric system has weaknesses and benefits. The system success depends on selecting the proper type of biometric modality to use and understanding how it affects system performance, cost, and limitations. Fingerprint holds the first place according to its popularity but facial recognition and iris recognition continue to grow for its accuracy and are considered as the future of biometric recognition. Besides biometric technology, other future possibilities for secure access control are- mobile access control, cloud-based security, and multi-factor authentication. This paper presents access control components, functions, and types. The aim of the paper is to discuss and analyze the latest biometric access control to secure sensitive information. This paper also provides an overview of access control, discusses the latest biometric technology, and presents the future trend of access control.

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

In today’s world many times need the human identification for the purpose of security of information, authentication, search criminals, find missing child. The biometric recognition uses for human identification because the biometrical characteristics are unique, and it’s based on two types are behavioural and physiological characteristics. This paper include biometric recognition system overview fingerprint recognition, face recognition, iris recognition, speech recognition and gesture recognition. overview include the various types of algorithm using in biometric recognition system are discrete cosine transform, key local variation, minutiae matching algorithm, PCA, active contour algorithm, fuzzy cmeans clustering, LPCC Algorithm, LDA Algorithm and Fuzzy Neural Network. The standard techniques implement following some of algorithms and recognition rate is near prediction about 100%. Keywords: Biometric Recognition, LDA, PCA Algorithms, Database.

Ambient Intelligence represents the medium with a huge number of connected heterogeneous and dynamic devices that respond to the presence of people. The development and implementation of technologies to ensure the security and reliability of such systems is the main task of many studies in this direction. One of the important technologies for the formation of a secure Ambient Intelligence is biometric systems. Biometric systems represent techniques for describing and measuring personal unique physiological and behavioral characteristics of a person and methods for applying them to automatic identification or authentication processes. In this paper, the basic principles and algorithms of a biometric identification system based on iris recognition are considered. The purpose of the research is to determine the structure of the security systems of Ambient Intelligence using biometric technologies.

Biometric systems have been using widely and these systems have improved substantially which are being used for persons Authentication and verification. This Biometrics is playing a key role in Personal, National and Global security. The main approach of this paper is to tell the importance of Biometric systems. Different types of biometric systems have been used these days. These biometric systems are iris recognition, palm vein biometrics, voice recognition, face recognition. The main threat to all these Biometric systems is Spoofing. Facial recognition biometric system is being widely used when compared to other biometric systems. Different types of facial spoofing include mask attack; photo attack & video attack and Different methods used to detect Face spoofing are discussed in this paper. These face spoofing detection techniques include the use of LBP, DMD, SVM, PReLU, IDA, LFHOG and CNN. The Other Techniques Include a Combination of LBP, CNN, and IDA. However, the results strongly prove that the Implementation of the above techniques will detect Face spoofing.

Inverse biometrics methods are a major privacy concern for users of biometric recognition systems. Affine‐based reconstruction attack is an inverse biometrics method that models the biometric recognition algorithm by an affine approximation. This type of attack reconstructs targeted biometric references using the modelled biometric recognition algorithm and the comparison scores issued by the system. Although this reconstruction method has only been successfully applied to reconstruct face images, the common consensus is that any biometric system that issues comparison scores could be vulnerable to such an attack since this method is sufficiently general to be applied to other biometric templates. Here it is shown that the attack fails to regenerate sparse vascular feature point templates. The reconstruction attack on feature point patterns extracted from retina and hand vascular images is tested. The inverse attack match rate for reconstructed reference templates was 0.3% in one experiment using retinal vasculature and 0% for all others. These results show that the reconstruction attack is not as catastrophic as it is widely accepted to be, and that vascular biometric template protection schemes that store sparse templates as references and reveal comparison scores are not susceptible to affine‐based reconstruction attacks.

Degradation in data quality is still a main source of errors in the modern biometric recognition systems. However, the data quality can be embedded in the recognition methods at global and local levels to build more accurate biometric systems. Local quality measures represent the quality of local parts within a biometric sample. They are either combined into a global quality measure or directly embedded into the recognition techniques. Minutiae-based comparison is the main and the most common technique used for fingerprint recognition and high-resolution palmprint recognition in various security and forensic applications. The focus of this thesis is mainly on direct incorporation of the local quality measures into the state-of-the-art minutiae-based recognition methods, particularly those based on Minutiae Cylinder-Code (MCC). Firstly, we introduce cylinder quality measures as a new type of local quality measures associated with the local minutiae descriptors. Then, we propose several methods for incorporating such local quality measures into the biometric systems, in order to improve their recognition performance. Among them is a novel and efficient quality-based consolidation method for embedding minutiae quality and cylinder quality measures in MCC based comparison methods. We also propose a supervised embedding method based on a binary classification model, which requires labeled minutiae for training. Finally, we apply a variant of the proposed consolidation method for the challenging case of latent fingerprint and palmprint identification with embedded subjective and objective minutiae quality.

In the modern digital world, biometric systems are a significant part of personal identification and access control. The use of sensitive biometric data such as iris and facial features raises huge privacy concerns because of the possibilities of data breaches and misuse. Therefore, this project focuses on the enhancement of the security of multimodal biometric systems through encryption mechanisms. The idea is to generate a strong biometric key based on features produced from iris and face biometric systems through advanced machine learning techniques. The features are then employed to produce a strong biometric key capable of encrypting the secret value using the AES algorithm. AES is a widely adopted symmetric encryption algorithm that ensures high efficiency in its software implementation and seamless processing capabilities of image data. This provides not only the reinforcement of authentication but also maintains confidentiality of biometric characteristics and mitigates the risks of privacy attacks and unauthorized access. Thus, this multimodal biometric encryption improves reliability, robustness, and the resistance power of the system against potential attacks, making it a promising step forward in the realm of secure biometric authenticationsystems. Keywords- Biometric encryption,Multimodal biometrics, ,Privacy attacks,Advanced Encryption Standard (AES), Biometric key generation,Iris recognition,Face recognition,Machine learning,Bio-crypto systems, Data security.