Context-Aware Adaptive Biometrics System using Multiagents

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.

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

With the use of computers and Internet in every major activity of our society, security is increasingly important. Biometric recognition is not only challenging but also computationally demanding. This chapter aims develop an iris biometric system. The iris has the advantages of uniqueness, stableness, anti-spoof, non-invasiveness and efficiency and could be applied in almost every area (banking, forensics, access control, etc.). The performance of a biometric classification system is largely depending on the techniques used for feature extraction. Inspired by the biological plausibility of ordinal measures, we propose their employment for iris representation and recognition. Qualitative measurement, associated to the relative ordering of different characteristics, is defined as ordinal measurement. Besides the proposing of a novel, fast and robust, ordinal based feature extraction method, the chapter also considers the problem of designing the decision making model so as to obtain an efficient and effective biometric system. In the literature, there are different approaches for iris recognition, nevertheless, there are still challenging open problems in improving the accuracy, robustness, security and ergonomics of biometric systems.

An electrocardiogram (ECG) biometric system can be wearable and offers continuous authentication. It is suitable for medical applications and has proved its benefits over traditional biometric systems. In this paper, we propose a solution to protect the ECG reference template from eavesdroppers spying the wireless link (between the wearable device and the target application) to preserve the privacy of the ECG reference template. The scheme fulfils the ISO/IEC 24745 biometric template protection requirements. The proposed solution is based on linear prediction coding of the ECG extracted features. We add a specific secret key to enhance unlinkability and renewability of the protected template. We evaluate the authentication performance of the unprotected and the protected system with a large database. Theoretical and empirical evaluations show that the proposed system outstands the fuzzy commitment scheme in terms of algorithmic complexity and energy consumption while preserving the same authentication performances proving its effectiveness, and efficiency.

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.

The use of biometric systems in physical access scenarios is gaining popularity. In such scenarios, users are enroled under well controlled conditions and the enrolment is usually indoors. To gain access to the building, the user provides his biometric samples in an outdoor environment over which there is little control. This adversely affects the quality of the samples and as a result the system performance is sub-optimal. This study evaluates the performance of a multimodal biometric system in a physical access scenario. We evaluate leading commercial algorithms on an indoor-outdoor, multimodal database comprising of face and voice samples. The indoor-outdoor nature of the database and the choice of modalities results in individual systems performing poorly. Popular normalization and fusion techniques are used to improve the performance of the overall system. Multimodal fusion results in an average improvement of approximately 20% at 1% false acceptance rate over individual modalities.

Score normalization applied to adaptive biometric systems

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.

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

Background: With the growing need for information safety and security rules in every corner of the globe. The biometric innovation has been used widely all over the world in daily life. In this view, the Multimodal-based biometric technique has gained popularity and interest due to its capability to resolve various problems associated with single-model biometric identification systems Methods:: In this research, an advanced multimodal-based biometric recognition system is introduced, which depends on multilayer perceptron (MLP), and it identifies humans using biometric features of Fingerprints and IRIS. To build an efficient model, the popular model called Rest- Net50 was used, the gradient method was used and the hinge technique was used as a loss function. For the technique of fusion, different techniques are used to show the effect of fusion in our model. The recognition rate of introduced systems was assessed by experimenting with various techniques on SDUMLA-HMT datasets based on the multimodal biometric datasets. The recognition rate showed that merging two biometric features in biometric recognition systems gives the best results compared to a single biometric feature. Results: The results obtained showed the superiority of our model with other existing techniques by obtaining a recognition rate of 99.79% with a feature-based fusion method. Conclusion: The introduced system used the MLP deep learning technique. The feature level fusion was applied to recognize the user using IRIS and fingerprints. To the best of our belief, this is the first research to incorporate MLP and multimodal-based biometric systems using IRIS and fingerprints.

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.

In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

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.

The Biometric system can be understood as a system that deals with an automated recognition of individual based on their physiological aspects (face, fingerprints, iris, retina) and behavioral patterns (signature, posture etc.). Biometric system works on feature extraction and feature matching. The feature is extracted in the form of fingerprints, iris and retina and then it is matched by the information stored by measuring the same patterns of particular individual, this process is feature matching. Between the two, works template database which is a central point from where every time the feature extracted is matched for confirming the identity of a person.. If the database is breached by the hacker, then the data could be used for falsifying the identity of the person. The paper focuses to implement blockchain technology in a biometric system in a manner that every record of individual is maintained using a blockchain so that it can’t be hampered by the hacker. Blockchain works as adecentralized repository of data which we assume to be the most suitable approach to hold the credentials of individuals and thus avoiding an unauthorized access to the systems. Making changes to blockchain is a complex and time-consuming task for any unwanted user. Many researchers contributed their work highlighting the security issues of the biometric system when applied practically. They noted that the fingerprint of an individual remains the same over life and once applied can’t be modified when compares with non-biometric systems which makes use of passwords and if forgotten or breached could be changed. Some of them conveyed that the biometric system is safe when only considering its physical implementation but the database created is still under threat.

Biometric identification referes to idetifying an individual based on his or her physilogical or beavioral characteristics. The use of more than one biometric identfiers in a biometric system, called the multimodal biometric system, increases the overall system accuracy and hence increase security, as well as reduce the enrollment problems. An effective and appropriate fusion strategy is needed to integrate different biometric information in such multimodal systems. This chapter provides an in-depth overview of traditional multimodal biometric systems and current trends in multimodal biometric fusion. Various approaches of rank level fusion, which is an not heavily investigated by researchers yet, are also illustrated in details in this chapter. Pros and cons of these rank fusion approaches are discussed which can be helpful for large scale multimodal biometric system deployment.