Biometric Authentication Framework Research Articles

A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0

The Internet of Things (IoT) is being applied in multiple domains, including smart homes and energy management. This work aims to tighten security in IoTs using fingerprint authentications and avoid unauthorized access to systems for safeguarding user privacy. Captured fingerprints can jeopardize the security and privacy of personal information. To solve privacy- and security-related problems in IoT-based environments, Biometric Authentication Frameworks (BAFs) are proposed to enable authentications in IoTs coupled with fingerprint authentications on edge consumer devices and to ensure biometric security in transmissions and databases. The Honeywell Advanced Encryption Security-Cryptography Measure (HAES-CM) scheme combined with Hybrid Advanced Encryption Standards with Chaotic Map Encryptions is proposed. BAFs enable private and secure communications between Industry 4.0’s edge devices and IoT. This work’s suggested scheme’s evaluations with other encryption methods reveal that the suggested HAES-CM encryption strategy outperforms others in terms of processing speeds.

A Novel Multimodal Biometric Authentication Framework Using Rule-Based ANFIS Based on Hybrid Level Fusion

A Novel Multimodal Biometric Authentication Framework Using Rule-Based ANFIS Based on Hybrid Level Fusion

BAMCloud: a cloud based Mobile biometric authentication framework

There has been an exponential increase in the number of users switching to mobile banking. Therefore, various countries are adopting biometric solutions as security measures. Biometric technologies provide the potential security framework to make banking more convenient and secure than it has ever been. These technologies are gaining much popularity because of the ease in capturing biometric data in real-time using one’s mobile phone. At the same time, the exponential growth of enrollment in the biometric system produces a massive amount of high-dimensional data. To overcome performance-related issues arising due to the resulting data deluge, this paper aims to propose a distributed mobile biometric system based on a high-performance cluster Cloud. In this paper, a Cloud-based mobile biometric authentication framework (BAMCloud) is proposed that uses dynamic signatures for authentication. The process flow of the BAMCloud system involves capturing data using any handheld mobile device, followed by its storage, preprocessing, and training of the system in a distributed manner over the Cloud. MapReduce has been implemented on the Hadoop platform to reduce the processing time. For model training, The Levenberg-Marquardt backpropagation neural network has been used. It achieves a speed of 8.5 times the original speed and performance of 96.23%. Furthermore, the cost-benefit analysis of the implemented system shows that the cost of implementation and execution of the system is less than the existing ones. The experiments demonstrate that better performance is achieved by implementing the proposed framework as compared to other methods used in recent literature.

A Framework for Multimodal Biometric Authentication Systems With Template Protection

A multimodal biometric authentication framework based on Index-of-Max (IoM) hashing, Alignment-Free Hashing (AFH), and feature-level fusion is proposed in this paper. This framework enjoys three major merits: 1) Biometric templates are secured by biometric template protection technology (i.e., IoM hashing), thus providing strong resistance to security and privacy invasion; 2) It flexibly adopts a variety of biometric feature representations (e.g., binary, and real-valued), thus generalizing to a wide range of biometric features for fusion; 3) Feature-level fusion, which has low template storage, low matching computational complexity, and low privacy risks, which can be accomplished without alignment via AFH. Specifically, the proposed framework works as a drag-and-drop model that can quickly adopt all popular biometric modalities with different feature distributions for feature-level fusion. The fused templates are produced using operators AND, OR and XOR in binary domain. To evaluate the proposed framework, benchmarking datasets from four widely deployed biometric modalities (i.e., FVC 2002 for fingerprint, LFW for face, CASIA-v3-Interval for iris, and UTFVP for finger-vein) are used. The experimental results presented in <xref ref-type="table" rid="table5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Table 5</xref> suggest that the proposed framework can achieve state-of-the-art performance in most of the datasets while offering additional folds, such as template protection and generalization to variable features. Moreover, biometric template protection criteria (irreversibility, unlinkability, and revocability) are also analyzed. The results of the analysis indicate satisfaction in terms of the security and privacy of the templates generated from the proposed framework.

A generic framework for deep incremental cancelable template generation

In a post-COVID-19 world, extensive study of deep learning-based biometric authentication techniques prompts the need to secure them. Further, the biometric data is assumed to be largely immutable; thus, if it is compromised, it is lost forever. Hence, reliable and secure biometric authentication is of utmost importance. In this paper, we address the security and privacy concerns of biometric templates generated via deep networks. We propose a cancelable biometric authentication approach. The framework consists of a lightweight Convolutional Neural Network (CNN) with a few-shot enrollment for generating biometric templates. Further, to enhance biometric templates’ discriminative power and to provide revocability, biometric templates are projected onto a random subspace (based on the user-specific key). Later projected biometric templates are mapped onto robust n-bit unique codes (using a KNN classifier) and protected via. SHA-3 hash digest. Moreover, a real-world biometric authentication system is always dynamic (users keep on changing). Thus we have also integrated phase-wise incremental learning within a deep learning-based cancelable biometric authentication framework. This is the first work in which deep cancelable templates are generated incrementally to the best of our knowledge. We analyze the proposed scheme for its performance and privacy preservation on three benchmarks constrained iris data-sets and over one unconstrained iris data-set along with one publicly available knuckle data-set. Furthermore, it has been demonstrated that the proposed cancelable incremental framework strictly follows the four fundamental properties of cancelability viz. non-invertibility, unlinkability, revocability, and usability.

Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm

Various cancelable biometric techniques have been proposed to maintain user data security. In this work, a cancelable biometric framework is introduced to satisfy user data security and keeping the original biometric template safe away from intruders. Thus, our main contribution is presenting a novel authentication framework based on the evolutionary Genetic Algorithm (GA)-based encryption technique. The suggested framework produces an entirely unrecognized biometric template by hiding the whole discriminative features of biometric templates; this is with exploiting the outstanding characteristics of the employed Genetic operations of the utilized encryption technique. Firstly, the GA initiates its search from a population of templates, not a single template. Secondly, some statistical operators are used to exploit the resulting initial population to generate successive populations. Finally, the crossover and mutation operations are performed to produce the ultimate cancelable biometric templates. Different biometric databases of the face and fingerprint templates are tested and analyzed. The proposed cancelable biometric framework achieves appreciated sensitivity and specificity results compared to the conventional OSH (Optical Scanning Holography) algorithm. It accomplishes recommended outcomes in terms of the AROC (Area under the Receiver Operating Characteristic) and the probability correlation distribution between the original biometrics and the encrypted biometrics stored in the database. The experimental results prove that the proposed framework achieves excellent results even if the biometric system suffers from different noise ratios. The proposed framework achieves an average AROC value of 0.9998, an EER (Equal Error Rate) of 2.0243×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , FAR (False Acceptance Rate) of 4.8843×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , and FRR (False Rejection Rate) of 2.2693×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> .

BioSec: A Biometric Authentication Framework for Secure and Private Communication Among Edge Devices in IoT and Industry 4.0

To address the privacy and security related challenges in Internet of Things (IoT) environment, we proposed a biometric method called BioSec to provide authentication in IoT integrated with edge consumer electronics with fingerprint authentication. Further, we ensured the security of biometric data both in the transmission channel and database with the standard encryption method. In this way, BioSec ensures secure and private communication among edge devices in IoT and Industry 4.0. Finally, we have compared three encryption methods used to protect biometric templates in terms of processing times and identified that AES-128-bit key encryption method outperforms others.

A New Framework for Match on Card and Match on Host Quality Based Multimodal Biometric Authentication

Smart cards are widely used to deploy secure and cost effective identity management systems. Integration of biometrics into the smart card leads to a strong two-factor authentication system through the match on card (MOC) process. Since MOC uses fixed authentication strategies during the life cycle of smart card, this leads to a low performance and high failure to acquire error in uncontrolled noisy environments. To solve this problem, this paper proposes a sequential quality based framework for biometric authentication. In the proposed framework a set of classifiers have been used to manage the workflow of the framework based on the quality of samples. Accordingly, subjects can be dynamically authenticated using MOC and MOH. A multimodal chimera database is used to evaluate this framework. Our findings indicate that the proposed approach provides higher accuracy than the unimodal MOC and MOH by 11.29% and 5.12%, respectively. Furthermore, the proposed framework can authenticate 83.85% of users without auxiliary trait at the expense of only 1.21% lower accuracy compared to parallel fusion, which require acquisition of all traits for entire users. Analysis of the results demonstrates that the proposed approach provides a compromise between accuracy, user convenience, security and system complexity.

Design of a Secure Biometric Authentication Framework Using PKI and FIDO in Fintech Environments

Design of a Secure Biometric Authentication Framework Using PKI and FIDO in Fintech Environments

Biometric Layering with Fingerprints: Template Security and Privacy Through Multi-Biometric Template Fusion

As biometric applications are gaining popularity, there is increased concern over the loss of privacy and potential misuse of biometric data held in central repositories. We present a biometric authentication framework that constructs a multi-biometric template by layering multiple biometrics of a user, such that it is difficult to separate the individual layers. Thus, the framework uses the biometrics of the user to conceal them among one another. The resulting biometric template is also cancelable if the system is implemented with cancelable biometrics, such as voice. We present a realization of this idea combining two or three different fingerprints of the user, using four different methods of template construction. Three of the methods use less and less information of the constituent biometrics, so as to lower the risk of leakage and cross-link rates. Results are evaluated on publicly available Finger Verification Championship (FVC) 2000, 2002 and NIST fingerprint databases. With the FVC databases, we obtain 2.1%, 3.9% and 3.4% Equal Error Rate on average using the three proposed methods, while the state-of-the-art commercial system achieves 1.9%. Furthermore, we show low cross-link rates under 63% under different scenarios, while genuine identification rates are 100%, with such a small gallery of 55 templates.

Secure Face Authentication Framework in Open Networks

In response to increased security concerns, biometrics is becoming more focused on overcoming or complementing conventional knowledge and possession-based authentication. However, biometric authentication requires special care since the loss of biometric data is irrecoverable. In this paper, we present a biometric authentication framework, where several novel techniques are applied to provide security and privacy. First, a biometric template is saved in a transformed form. This makes it possible for a template to be canceled upon its loss while the original biometric information is not revealed. Second, when a user is registered with a server, a biometric template is stored in a special form, named a ‘soft vault’. This technique prevents impersonation attacks even if data in a server is disclosed to an attacker. Finally, a one-time template technique is applied in order to prevent replay attacks against templates transmitted over networks. In addition, the whole scheme keeps decision equivalence with conventional face authentication, and thus it does not decrease biometric recognition performance. As a result, the proposed techniques construct a secure face authentication framework in open networks.

Non-intrusive liveness detection by face images

A technique evaluating liveness in face image sequences is presented. To ensure the actual presence of a live face in contrast to a photograph (playback attack), is a significant problem in face authentication to the extent that anti-spoofing measures are highly desirable. The purpose of the proposed system is to assist in a biometric authentication framework, by adding liveness awareness in a non-intrusive manner. Analyzing the trajectories of certain parts of a live face reveals valuable information to discriminate it against a spoofed one. The proposed system uses a lightweight novel optical flow, which is especially applicable in face motion estimation based on the structure tensor and inputs of a few frames. For reliable face part detection, the system utilizes a model-based local Gabor decomposition and SVM experts, where selected points from a retinotopic grid are used to form regional face models. Also the estimated optical flow is exploited to detect a face part. The whole procedure, starting with three images as input and finishing in a liveness score, is executed in near real-time without special purpose hardware. Experimental results on the proposed system are presented on both a public database and spoofing attack simulations.