Biometric Recognition on Compressed ECG Signals Under Different Physiological Conditions

In the study, ECG is investigated as a biometric recognition system. To fit in the real-life scenarios, different physiological conditions and signal compression are considered. A total of 9 subjects conducting three simple exercises which are riding bicycle on high resistance, low resistance and walking taken from Physionet. Due to the noise collected during ECG recording process, the contaminated signals are filtered using low pass Butterworth filter. Then, QRS is segmented using Pan Tompkins algorithm. After that, Wavelet Transform is applied to compress the segmented QRS. Classification is done on non-compressed and compressed QRS by using multi-class of SVM to compare its accuracy. The study discovered that, biometric can be performed on compressed signals. The outcome also shows that, Cubic SVM is the proper classification technique to be applied on person verification regardless of different physiological conditions. The accuracy achieved was up to 94.4% for non-compressed signals and 91.7% for compressed signals.

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 Most student identification cards outdated personal information.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.

Face recognition is one of the fastest-growing technology in the field of biometrics, electronic business (e-business), and image processing. With the increase of e-business, the lack of transaction security has led to the increase in fraudulent activities. Most systems such as e-business transactions require a reliable recognition system to provide services to authentic and legitimate users. Biometric recognition using facial traits is the solution to dealing with the security issues in e-business transactions. Biometric recognition is the process of digitally recognizing a person based on their physiological or behavioral traits. Face recognition technology is applied in the fields like e-business, home security, and criminal identification. This paper proposes the implementation of the face recognition system for login in e-business transactions. A three-phase enhanced e-business security system that consists of face detection, feature extraction, and face recognition is designed to ensure that the security in e-business transactions is always maintained. The biometric face recognition system will reduce the transaction security issues in e-business. The python OpenCV library was used to simulate and evaluate the effectiveness of the face recognition system. Experimental results are provided to illustrate the effectiveness of the face recognition system.

Biometric systems have been used extensively in the identification and verification of persons. Fingerprint biometrics stands out as the most effective due to their characteristics of Permanence, uniqueness, ergonomics, throughput, low cost, and lifelong usability. By reducing the number of comparisons, biometric recognition systems can effectively deal with large‐scale databases. Fingerprint classification is an important task used to reduce the number of comparisons by dividing fingerprints into classes. Deep learning models have demonstrated impressive classification performance in fingerprint classification tasks. The high‐level features of deep learning models can affect the transfer learning in deep learning models. Furthermore, the high‐level features involve high computational costs that can render difficulty in the deployment of the applications. This work proposes an improved system for fingerprint classification through the truncation of layers and transfer learning. Our approach modifies the ResNet50 model to improve its network inference speed and performance in fingerprint classification by removing some deep convolutional layers. We then finetune the modified model and train it using a fingerprint dataset. The results show that the finetuned modified model improves classification accuracy at a reduced computational cost. At only 5.1M parameters, our model obtained a classification accuracy of 93.3% and precision of 93.4% performing better than previous studies based on its size‐performance ratio.

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.

Biometric recognition systems are finding applications in more and more civilian fields because they proved to be reliable and accurate. Among the other technologies, ultrasound has the main merit of acquiring 3D images, which allows it to provide more distinctive features and gives it a high resistance to spoof attacks. This work reviews main research activities devoted to the study and development of ultrasound sensors and systems for biometric recognition purposes. Several transducer technologies and different ultrasound techniques have been experimented on for imaging biometric characteristics like fingerprints, hand vein pattern, palmprint, and hand geometry. In the paper, basic concepts on ultrasound imaging techniques and technologies are briefly recalled and, subsequently, research studies are classified according to the kind of technique used for collecting the ultrasound image. Overall, the overview demonstrates that ultrasound may compete with other technologies in the expanding market of biometrics, as the different commercial fingerprint sensors integrated in portable electronic devices like smartphones or tablets demonstrate.

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.

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.

Biometrics is used to identify individual in groups by their characteristics, such as fingerprint, face, iris, voice and behavioral characteristics like hand written signature and keystroke. Normally, a majority of biometrics recognition systems adopt methods by using unique feature to discriminate different individual from groups. But there is some forgery, subjective modifications like making up exiting as usual. A multimodal biometric recognition systemusing multiple biometrics to identify is proposed. In addition, it can support better Equal Error Rate (EER), False Acceptance Rate (FAR) and False Rejection Rate (FRR) by combining two or more physical or behavioral features. With lots of approached have been proposed, experts also pay much attention to the methods mentioned in recent years. The objective of this paper is to support a survey of multimodal biometric recognition system and approach using face, fingerprint recognition and enhanced iris characters that commonly used recently.

A biometric recognition system is one of the leading candidates for the current and the next generation of smart visual systems. The visual system is the engine of the surveillance cameras that have great importance for intelligence and security purposes. These surveillance devices can be a target of adversaries for accomplishing various malicious scenarios such as disabling the camera in critical times or the lack of recognition of a criminal. In this work, we propose a cross-layer biometric recognition system that has small computational complexity and is suitable for mobile Internet of Things (IoT) devices. Furthermore, due to the involvement of both hardware and software in realizing this system in a decussate and chaining structure, it is easier to locate and provide alternative paths for the system flow in the case of an attack. For security analysis of this system, one of the elements of this system named the advanced encryption standard (AES) is infected by four different Hardware Trojansthat target different parts of this module. The purpose of these Trojans is to sabotage the biometric data that are under process by the biometric recognition system. All of the software and the hardware modules of this system are implemented using MATLAB and Verilog HDL, respectively. According to the performance evaluation results, the system shows an acceptable performance in recognizing healthy biometric data. It is able to detect the infected data, as well. With respect to its hardware results, the system may not contribute significantly to the hardware design parameters of a surveillance camera considering all the hardware elements within the device.

The biometrics is an alternative for password-based recognition systems in recent days. But biometric recognition systems are venerable to spoof attacks. Hence, anti-spoofing techniques are attracting growing interest in this field. Liveness detection is the anti-spoofing technique, which has the aim to identify the dead and living modalities presented at the time of authentication. This paper presents an efficient and robust multimodal biometric system integrated with liveness detection technique. In the proposed system, face and fingerprint modalities are used at the enrolment time and only face modality is used to authenticate a person. Here, fingerprint is used to generate a fingerprint key using K-means clustering algorithm and then encrypt the face feature set with asymmetric encryption standard (AES) using fingerprint key. At the time of authentication, liveness of a person is checked using challenge--response method at the sensor level. If in any case, an intruder has passed liveness test, he/she may be detected at matching score level as template of system is secured with AES encryption. Multimodal biometric system integrated with liveness detection technique can increase the speed of the system. Matrix Laboratory (MATLAB) 2017b and multi-biometric integration (MUBI) tool are used for the experimental work, and results show that the proposed system gives better performance as compared to others.KeywordsFace recognitionEncrypted face templateLiveness detectionMultimodal biometric system

With the widespread deployment of biometric recognition systems, the interest in attacking these systems is increasing. One of the easiest ways to circumvent a biometric recognition system are so-called presentation attacks, in which artefacts are presented to the sensor to either impersonate another subject or avoid being recognised. In the recent past, the vulnerabilities of biometric systems to so-called morphing attacks have been unveiled. In such attacks, biometric samples of multiple subjects are merged in the signal or feature domain, in order to allow a successful verification of all contributing subjects against the morphed identity. Being a recent area of research, there is to date no standardised manner to evaluate the vulnerability of biometric systems to these attacks. Hence, it is not yet possible to establish a common benchmark between different morph detection algorithms. In this paper, we tackle this issue proposing new metrics for vulnerability reporting, which build upon our joint experience in researching this challenging attack scenario. In addition, recommendations on the assessment of morphing techniques and morphing detection metrics are given.

It is an undeniable fact that biometric recognition systems are susceptible to different software-related or template attacks at different vulnerable points in the biometric authentication system. However, these attacks are frequently overlooked, implying one of two possibilities: either researchers and biometric stakeholders have forgotten or simply ignored the consequences of these attacks, or they are unaware of some of these threats. Meanwhile, if biometric systems do not have the required protection, these attacks can lead to biometric system compromise and consequently the loss of sensitive personal data, which is irreplaceable and irrevocable. Therefore, considering the significance of the implications posed by these threats, researchers and biometric stakeholders have, over the last two decades, proposed different methods to mitigate some of the attacks, while a large number of the existing attacks are left unattended to. Hence, we deemed it paramount to bring this to the attention of the research community. This article is an effort to emphasize the implications of these attacks, exploit the threat approach based on attackers’ knowledge, and consolidate them into a single, thorough, and comprehensive review together with recent state-of-the-art protection mechanisms that are put forward to mitigate these threats. Moreover, a comparative performance analysis of these protection mechanisms and other possible countermeasures to the attacks is given. It is expected that this survey will, on the one hand, be a wakeup call to the biometric template protection research community, thus drawing their attention to the task at hand. On the other hand, an awareness to the ordinary users and biometric stakeholders on the risks posed by these threats.

In this work two practical issues of biometric log recognition using log end images are investigated: Temporal and longitudinal variances of log cross-sections (CSs). These variances are related to the requirement of robustness for biometric characteristics. A texture feature-based fingerprint matching technique is used to compute matching scores between CS images. Our experimental evaluation is based on the temporal and longitudinal variances of 35 slices of a single tree log which where captured at four time delayed sessions. Results indicate, that biometric log recognition using log end images is robust and is able to overcome both issues. This work contributes to the development of a biometric log recognition system by showing that a texture feature-based matching technique is applicable to log CSs.

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.