Multibiometric Systems Research Articles

Multimodal Biometric Authentication for Smartphones

Abstract: Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amount of freedom, lack of universality and other factors. Therefore, multimodal biometric systems have come into existence in order to increase security as well as to achieve better performance.[1] This paper provides an overview of different multimodal biometric (multibiometric) systems for smartphones being employed till now and also proposes a multimodal biometric system which can possibly overcome the limitations of the current biometric systems. Keywords: Biometrics, Unimodal, Multimodal, Fusion, Multibiometric Systems

Hand-based multibiometric systems: state-of-the-art and future challenges.

The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

A Privacy Preserving Based Multi-Biometric System for Secure Identification in Cloud Environment

A Privacy Preserving Based Multi-Biometric System for Secure Identification in Cloud Environment

Design A Multi Biometric System for Safe Access to Buildings

Designing a multi-biometric system for safe access to buildings is a very critical process because this system will act as an alternative of humans in observation, and must make smart decisions to protect the building from any intruder depending on the biometric information of the entering persons. In this paper a full design of a proposed multi-biometric system will be presented, two sensors are used (Visible and Thermal). the proposed system consists of several subsystems The first is the construction of a database that contains all the photos and information about each person who belongs to the building, the central system using visible and thermal cameras, and the peripheral system using visible cameras only. The central system is capable of remotely identifying and ensure of the health condition (Temperature, Heart Rate, Respiration Rate) of each entering person which is a very important process, especially with the COVID-19 pandemic. The peripheral system monitors any suspicious issue like crowed or walking outside allowed lines.

Multibiometric System for Iris Recognition Based Convolutional Neural Network and Transfer Learning

Multimodal biometric methods have been commonly used by several implementations because of its capability to work with a variety of important drawbacks in unimodal biometric methods, such as noise affectability, populace coverage, intraclass variety, vulnerability to spoofing, and non-universality. In this research, a multimodal biometric realtime method is suggested depending upon the design of a deep learning model for pictures of a person’s (right & left) irises. This system has been implemented by combining the characteristics of convolution neural networks and transfer learning techniques. Through this research, the training system focused on a collection of the back_propagation technique with Adam’s optimization approach utilized to modify weights and adjust learning rates during the learning process. The efficiency of the system is examined on two public datasets obtained in various conditions: IITD and CASIA-Iris-V3 Interval. The implemented system gives an accuracy of 99% for both left & right IITD iris datasets and accuracy of (94% and 93%) for the left and right iris for CASIA-iris-V3 interval datasets respectively after training. An OpenCV library for image pre-processing, Keras, and sci-kit learn python libraries for feature extraction and recognition has been utilized.

Ann trained and WOA optimized feature-level fusion of iris and fingerprint

“Uni Uni-modal Biometric systems has been widely implemented for maintaining security and privacy in various applications like mobile phones, banking apps, airport access control, laptop login etc. Due to Advancement in technologies, imposters designed various ways to breach the security and most of the designed biometric applications security can be compromised. The quality of input sample also play an important role to attain the best performance in terms of improved accuracy and reduced FAR & FRR. Researchers has combined the various biometrics modalities to overcome the problems of Uni-modal biometrics. In this paper, a multi biometric feature level fusion system of Iris, and Fingerprint is presented. Due to consistency feature of fingerprint and stability feature of iris modality taken into consideration for high security applications. At pre-processing level, the atmospheric light adjustment algorithm is applied to improve the quality of input samples (Iris and Fingerprint). For feature extraction, the nearest neighbour algorithm and speedup robust feature (SURF) is applied to fingerprint and Iris data respectively. Further, for selecting the best features, the extracted features are optimized by GA algorithm. To achieve an excellent recognition rate, the iris and fingerprint data is trained by ANN algorithm. The experimental results show that the proposed system exhibits the improved performance and better security. Finally, the template is secured by applying the AES algorithm and results are compared with DES, 3DES, RSA and RC4 algorithm.

Optimized multi‐biometric enhancement analysis

A multi-biometric system uses different modalities to identify individuals more accurately. The authors analyse fusion efficiency of a significant number of multi-biometric fusion schemes. To do so, the study applies different functions that are generated using genetic programming (GP) on the 2000 multi-biometric instances produced by the fusion of different biometric matching scores. The functions are represented using a tree of arithmetic operations and are used for fusion at score level. First, genetic programming is implemented on the XM2VTS score database. The GP optimizes the half total error rate of fused matching scores. Then, a comparative study is performed based on our experiments on matching scores of different biometric baseline systems provided by the bio-secure database. This database provides 24 streams that we use to generate 2000 multi-biometric combinations. These multi-biometric instances combine matching scores of different instances, sensors and traits. To assess the quality of the fused scores and the quality of performing biometric baseline systems, we use weighted functions based on user-specific and group-specific normalization. Then, we propose a hybrid cat swarm optimization (CSO) based on the average-velocity inertia-weighted CSO and the normal mutation strategy-based CSO to compute the weights of the selected functions for the fused biometric systems. Finally, we present the statistical significance tests to confirm that the proposed functions outperform the existing functions based on arithmetic rules, normalization fusion and evolutionary algorithms.

Energy‐efficient multi‐biometric system for Internet of Things using trust management

The traditional password-based authentication systems are easy to breach because they rely on what the user knows and not on who is the user. This makes them prone to impersonation, as the culprit can gain access to the system once they knew the user's password, which led to the development of biometric-based authentication systems. Nonetheless, noisy data, intra-, and inter-class variations render these systems inaccurate. However, combining multiple biometric traits (multi-biometric systems) promises to increase the accuracy of biometric systems. But, this leads to an overhead in energy and latency in the system. These overheads are frowned at in embedded systems, especially those deployed in remote areas. Here, the authors propose a trust management-based multi-biometric system. The system switches between a uni-biometric and a multi-biometric system to achieve high accuracy and reduced overhead (i.e. energy and latency). The system uses the uni-biometric technique for trusted users and the multi-biometric method for untrustworthy users who are zero-effort impostors. It is found to reduce the false positive rate by 0.29 of Fingerprint uni-biometric system and the false negative rate by a factor of 0.5 of the multi-biometric system. Also, the energy consumption is reduced by a factor of 0.2 of the multi-biometric system.

Adaptive Management of Multimodal Biometrics—A Deep Learning and Metaheuristic Approach

This paper introduces the framework for adaptive rank-level biometric fusion: a new approach towards personal authentication. In this work, a novel attempt has been made to identify the optimal design parameters and framework of a multibiometric system, where the chosen biometric traits are subjected to rank-level fusion. Optimal fusion parameters depend upon the security level demanded by a particular biometric application. The proposed framework makes use of a metaheuristic approach towards adaptive fusion in the pursuit of achieving optimal fusion results at varying levels of security. Rank-level fusion rules have been employed to provide optimum performance by making use of Ant Colony Optimization technique. The novelty of the reported work also lies in the fact that the proposed design engages three biometric traits simultaneously for the first time in the domain of adaptive fusion, so as to test the efficacy of the system in selecting the optimal set of biometric traits from a given set. Literature reveals the unique biometric characteristics of the fingernail plate, which have been exploited in this work for the rigorous experimentation conducted. Index, middle and ring fingernail plates have been taken into consideration, and deep learning feature-sets of the three nail plates have been extracted using three customized pre-trained models, AlexNet, ResNet-18 and DenseNet-201. The adaptive multimodal performance of the three nail plates has also been checked using the already existing methods of adaptive fusion designed for addressing fusion at the score-level and decision-level. Exhaustive experiments have been conducted on the MATLAB R2019a platform using the Deep Learning Toolbox. When the cost of false acceptance is 1.9, experimental results obtained from the proposed framework give values of the average of the minimum weighted error rate as low as 0.0115, 0.0097 and 0.0101 for the AlexNet, ResNet-18 and DenseNet-201 based experiments respectively. Results demonstrate that the proposed system is capable of computing the optimal parameters for rank-level fusion for varying security levels, thus contributing towards optimal performance accuracy.

A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion

A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion

A multibiometric system based on the fusion of fingerprint, finger-vein, and finger-knuckle-print

Authentication systems play an essential role in our lives today. Human biological, behavioral, and morphological characteristics are usually used in an authentication system in a vast scope of applications ranging from unlocking consumer devices to surveillance and forensic analysis and offered an alternative to credit cards, ID cards, passports, driving licenses, etc. Unibiometric systems, which use a single biometric modality, suffer from some drawbacks such as low protection of user’s privacy against attacks. Multibiometric systems, which fuse features of biometric characteristics, can cope with unibiometric systems’ drawbacks and improve security and recognition accuracy. However, some main questions such as ‘what is the optimal number of biometric modalities?’, ‘how much accuracy do we need for our system?’, and ‘how much money do we want to invest for our system?’ have to be considered and answered before designing and implementing a multibiometric system. Unfortunately, the existing multibiometric systems have not considered and responded to all questions. Furthermore, identification mode for multibiometric systems is a challenging task and almost all of them have focused on verification mode. In this paper, we only consider a finger and employ the maximum possible modalities of the finger, i.e., fingerprint, finger-vein, and finger-knuckle-print, to increase user-friendliness and reducing the cost of implementation of the system. The proposed system only uses three cameras to capture all contactless fingerprint, finger-vein, and finger-knuckle images. Also, we propose an algorithm that makes enable the system to work in identification mode. The experiments on established databases exhibit that our proposed algorithm significantly increases recognition accuracy.

Effectiveness of Deep Learning on Serial Fusion Based Biometric Systems

We develop a framework for multibiometric systems, which combines a deep learning technique with the serial fusion method. Deep learning techniques have been used in unimodal and parallel fusion-based multimodal biometric systems in the past few years. While deep learning techniques have been successful in improving the authentication accuracy, a biometric system is still challenged by two issues: 1) a unimodal system suffers from environmental interference, spoofing attacks, and nonuniversality, and 2) a parallel fusion-based multimodal system suffers from user inconvenience as it requires the user to provide multiple biometrics, which in turn takes longer verification times. A serial fusion method can improve user convenience in a multibiometric system by requiring a user to submit only a subset of the available biometrics. To our knowledge, the effectiveness of using a deep learning technique with a serial fusion method in multibiometric systems is still underexplored. In this article, we close this research gap. We develop a three-stage multibiometric system using a user's fingerprint, palm, and face and test three serial fusion methods with a Siamese neural network. Our experiments achieve an AUC of 0.9996, where the genuine users require only 1.56 biometrics (instead of all 3) on an average. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact statement—</i>We work on enhancing the user convenience and reducing the verification error in a multibiometric system. An improved multibiometric system can help law enforcement, homeland security, defense, and our daily lives by providing better access control. With the advent of deep learning technologies, the accuracy of multibiometric systems have been improved significantly; however, its applicability is still in question because of long verification times required by parallel fusion in a multibiometric system. Our proposed multibiometric framework alleviates this user inconvenience issue by utilizing a serial fusion strategy in decision making and improves accuracy by leveraging deep learning technology in feature extraction and score generation.

Palm-Vein Verification Using Images From the Visible Spectrum

In this paper, we investigate into utilization of images from the visible light (RGB) spectrum for identity verification based on the palm-veins. This is differentiated from the commonly utilized Near-infrared (NIR) images for palm-vein feature extraction. Our goal is to explore into the often omitted palm-vein information from the RGB palm images considering the vast deployment of the RGB cameras. Essentially, the vein line features are extracted at various scales based on an efficient difference image projection. The extracted features from the gallery and the probe images are matched based on a fast Hamming distance implementation. The resultant similarity scores are finally fused at score level for accuracy enhancement. Experiments are conducted on two public multi-spectral palm databases. The results show encouraging matching accuracy and computational efficiency of the proposed method which extracts the palm-vein utilizing only the visible spectrum. The outcome of this study can be deployed as a standalone biometric or as part of a multibiometric system for secure authentication.

A hybrid multimodal biometric scheme based on face and both irises integration for person authentication

In the biometric field, mono-modal biometry suffers from multiple lacks. The use of multiple biometrics has helped getting over those limitations and has allowed outperforming single biometrics in terms of recognition rate. In this paper, we propose a new approach that fuses the Gabor-Winger transform and the oriented Gabor phase information for feature extraction as well as a hybrid scheme consisting of multi-algorithm, multi-instance and multi-modal systems that integrates face and both left and right irises of the same subject. The SDUMLAHMT database has been used to evaluate the proposed approach. The results show that our multi-modal biometric system achieves higher accuracy than the single biometric approaches as well as the other existing multi-biometric systems.

A hybrid multimodal biometric scheme based on face and both irises integration for person authentication

In the biometric field, mono-modal biometry suffers from multiple lacks. The use of multiple biometrics has helped getting over those limitations and has allowed outperforming single biometrics in terms of recognition rate. In this paper, we propose a new approach that fuses the Gabor-Winger transform and the oriented Gabor phase information for feature extraction as well as a hybrid scheme consisting of multi-algorithm, multi-instance and multi-modal systems that integrates face and both left and right irises of the same subject. The SDUMLAHMT database has been used to evaluate the proposed approach. The results show that our multi-modal biometric system achieves higher accuracy than the single biometric approaches as well as the other existing multi-biometric systems.

Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies

Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to higher person recognition accuracy; besides maintaining hygienic and pandemic concerns. Towards this aim, in this paper, an effective multi-biometric scheme for person authentication based on contactless fingerprint and palmprint selfies has been proposed. In this study, for simplicity and efficiency, three local descriptors, i.e., local phase quantization (LPQ), local Ternary patterns (LTP), and binarized statistical image features (BSIF), have been employed to extract salient features from contactless fingerprint and palmprint selfies. The score level fusion based multi-biometric system developed in this work combines the matching scores using two different fusion techniques, i.e., transformation based-rules like triangular norms and classifier based-rules like SVM. Experimental results on two publicly available databases (i.e., PolyU contactless to contact-based fingerprint database and IIT-Delhi touchless palmprint dataset) show that the proposed contactless multi-biometric selfie system can easily outperform uni-biometrics.

PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT – a case study

Pervasive and useR fOcused biomeTrics bordEr projeCT (PROTECT) is an EU project funded by the Horizon 2020 research and Innovation Programme. The main aim of PROTECT was to build an advanced biometric-based person identification system that works robustly across a range of border crossing types and that has strong user-centric features. This work presents the case study of the multibiometric verification system developed within PROTECT. The system has been developed to be suitable for different borders such as air, sea, and land borders. The system covers two use cases: the walk-through scenario, in which the traveller is on foot; the drive-through scenario, in which the traveller is in a vehicle. Each deployment includes a different set of biometric traits and this study illustrates how to evaluate such multibiometric system in accordance with international standards and, in particular, how to overcome practical problems that may be encountered when dealing with multibiometric evaluation, such as different score distributions and missing scores.

Implementation face based cancelable multi-biometric system

This paper suggests novel two proposed cancellable biometric realization techniques recognition and template protection. In this paper, the Homomorphic Key (HK) encoding algorithm is utilized for cancelable face system. In the first suggested scheme, the HK algorithm is applied on the face images. The resultant map is encrypted, in order to the second HK utilized in the HK is produced from the image. This approach can be used to develop a frequency domain procedure for making this system for biometric template protection. The second algorithm presents a new approach to detect the features of face images depending the Speeded Up Robust Features (SURF) and Optimum Global Thresholding (OTSU) method. This algorithm is relied on scale space analysis with the number of SURF feature points as the key parameters for classification. Simulation results using evaluation metrics False Positive Rate (FPR), False Negative Rate (FNR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Area under ROC (AROC) prove that the first proposed cancelable biometric technique with the second key are best with comparing the other keys. The obtained results clear that the second suggested technique has sucesseded in detection the features of the skin, eyes, nose, hair, ears images and the face positions.

Multi-modal biometric system using finger knuckle image and retina image with template security using PolyU and DRIVE database

A wide variety of security applications like computers, laptops, mobile phones, ATM machines, physical access control to property etc. are using biometrics based system for human identification. Today’s Biometrics authentication system can provide highest level of security to the user of the service and the service providers. Today’s uni-modal biometric systems has become mature system and being used for low level to medium level security applications. But, for extremely higher level of security requirements like military base, nuclear plants, research laboratories etc., Finger knuckle image and Retina image based combination can be used, as this combinations provides the highest level of accuracy, low error rates, universality etc. Also the security of Multi-modal biometric templates is very importance as biometric template store vital information of the user. In this research work, Multi-modal Biometric system for higher level security using Finger Knuckle image and Retina image with templates security is developed, experiments are carried out and results are calculated using PolyU database for Finger Knuckle image and DRIVE for Retina images. A Finger Knuckle Image processing starts with Median Filtering, CLAHE method and Canny edge detection. The features extraction is carried using SURF feature method. Once the finger knuckle image template is generated, it is encrypted using RSA algorithm and stored in database. The Retina Image Processing is carried out by applying Green channel followed by Gaussian filtering, CLAHE method is applied on outcome of Gaussian filtering. SURF features set is extracted from retina ROI. Retina image template is generated which is later encrypted using RSA algorithm. The final experiment shows that genuine acceptance rate = 100% is achieved for threshold value 0.35 and False Acceptance Rate is only 1%. The scientific contribution of proposed research work is a multi-biometric system with higher accuracy (GAR is 100%), low error rate (FAR is 1%), better template protection by RSA encryption and efficient hybrid model using finger knuckle image and retina image.

Enhancement of Individuality Representation for Multi-Biometric Identification

Personal identification is one of the areas in pattern recognition that has created a center of attention by many researchers to work in. Recently, its focal point is in forensic investigation and biometric identification as such the physical (i.e., iris, fingerprint) and behavioural (i.e., signature) style can be used as biometric features for authenticating an individual. In this study, an improved approach of presenting biometric features of true individual from multi-form of biometric images is presented. The discriminability of the features is proposed by discretizing the extracted features of each person using improved Biometric Feature Discretization (BFD). BFD is introduced for features perseverance to obtain better individual representations and discriminations without the use of normalization. Our experiments have revealed that by using the proposed improved BFD in Multi-Biometric System, the individual identification is significantly increased with an average identification rate of 98%.