Multimodal Biometric System Research Articles

Robust Multimodal Biometric System Based on Feature Level Fusion of Optimiseddeepnet Features

Robust Multimodal Biometric System Based on Feature Level Fusion of Optimiseddeepnet Features

INTELLIGENT sYSTEM FOR HUMAN AUTHENTICATION USING FUSION OF DORSAL HAND, PALM AND FINGER VEINS

Multimodal biometric systems roughly used to achieve extreme recognition accuracy. This paper reports a novel multimodal biometric system employing intelligent technique to authenticate human by fusion of dorsal hand, palm and finger veins pattern. We improved an image analysis technique to separate region of interest (ROI) from dorsal hand, palm and finger veins image. After separating ROI we construct a sequence of preprocessing steps to enhance dorsal hand, palm and finger veins images using Median filter, Wiener filter, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Homomorphic filter to improve vein image. Our intelligent technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database selected was Bosphorus Hand Vein Database, CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) and the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT). The accomplished result for the fusion of three biometric traits was Correct Recognition Rate (CRR) is 99.21% with False Reject Rate (FRR) 0.04%.

Human Authentication using Face, Voice and Fingerprint Biometrics

Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the system consider the person as authorized person. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.

Intelligent Technique for Human Authentication using Fusion of Finger and Dorsal Hand Veins

Multimodal biometric systems have been widely used to achieve high recognition accuracy. This paper presents a new multimodal biometric system using intelligent technique to authenticate human by fusion of finger and dorsal hand veins pattern. We developed an image analysis technique to extract region of interest (ROI) from finger and dorsal hand veins image. After extracting ROI we design a sequence of preprocessing steps to improve finger and dorsal hand veins images using Median filter, Wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance vein image. Our smart technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database chosen was the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) and Bosphorus Hand Vein Database. The achieved result for the fusion of both biometric traits was Correct Recognition Rate (CRR) is 96.8%.

Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature

Biometric identification technology is widely utilized in our everyday lives as a result of the rising need for information security and safety laws throughout the world. In this aspect, multimodal biometric recognition (MBR) has gained significant research attention due to its ability to overcome several important constraints in unimodal biometric systems. Henceforth, this research article utilizes multiple features such as an iris, face, finger vein, and palm print for obtaining the highest accuracy to identify the exact person. The utilization of multiple features from the person improves the accuracy of biometric system. In many developed countries, palm print features are employed to provide the most accurate identification of an actual individual as fast as possible. The proposed system can be very suitable for the person who dislikes answering many questions for security authentication. Moreover, the proposed system can also be used to minimize the extra questionnaire by achieving a highest accuracy than other existing multimodal biometric systems. Finally, the results are computed and tabulated in this research article.

Multi-Modal Biometrics Systems: Concepts, Strengths, Challenges and Solutions

Authentication is a very important aspect of computer security. Most systems employ strategies such as Password-based authentication, Multi-factor authentication, Certificate based authentication which are accompanied with a lot ofchallenges. To address this issue, most security systems have introduced the use of biometrics for authentication. Unimodal biometrics systems have many limitations regarding performance and accuracy. The use of Multimodal biometrics systems for authentication is recently attracting the attention of researchers due to its capacity to overcome most of the drawbacks of Unimodal biometric systems. This paper focuses on the use of biometric technology for authentication. The strengths of multimodal biometric systems, together with the challenges of multimodal biometric systems are presented. The paper also suggests solutions to the challenges of multimodal biometric systems.

Cascade‐based Multimodal Biometric Recognition System with Fingerprint and Face

AbstractIn recent years, biometrics has played a vital role in protecting users' privacy and enabling secure authentication. Multimodal security, comprised of identity cards with attached unique passwords, is used to authenticate the genuine or imposter person; however, it is not a perfect security framework. Biometric traits are unique to every individual and hence proved to be very secure. This paper proposes a hybrid approach by combining cascaded and fusion‐based multimodal biometric framework using fingerprint and face traits. The fingerprint and face features are extracted using the minutiae feature extraction algorithm and principal component analysis (PCA) algorithm. The hybrid approach is applied to a self ‐built database of around 450 fingerprints and 450 face images. The performance of the proposed hybrid system is determined using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), and Accuracy evaluation parameters. The unimodal system of fingerprint and face at Level I and Level II yields an accuracy of 100% at a threshold value of 0.42 and 0.54, respectively. The multimodal system at Level III delivers an accuracy rate of 93.70%, 99.20%, 97.85%, and 99.50% at a threshold value of 0.75, 0.60, 0.30, and 0.60 in sum level fusion, product level fusion, min level fusion, and max level fusion schemes, respectively.

A Tokenless Cancellable Scheme for Multimodal Biometric Systems

Biometric template protection (BTP) is an open problem for biometric identity management systems. Cancellable biometrics is commonly designed to protect biometric templates with two input factors i.e., biometrics and a token used in template replacement. However, the token is often required to be kept secretly; otherwise, the protected template could be vulnerable to several security attacks and breaches of privacy. In this paper, we propose a tokenless cancellable biometrics scheme called Multimodal Extended Feature Vector (M∙EFV) Hashing that employs an improved XOR encryption/decryption notion to operate on the transformation key. We stress on multimodal biometrics where the real-valued face and fingerprint vectors are fused and embedded into a binarized cancellable template. Specifically, M∙EFV hashing consists of three stages of transformation: 1) normalization and biometric fusion; 2) randomization and binarization; and 3) cancellable template generation. To evaluate the proposed scheme, several benchmarking datasets, i.e., FVC2002, FVC2004 for fingerprint and LFW for face are used in experiments. The verification performance is validated by employing the FVC matching protocol. Various attacks are simulated and analysed in the worst-case scenario. Lastly, unlinkability and revocability properties are examined experimentally.

User recognition based on periocular biometrics and touch dynamics

• We proposed a multimodal biometric recognition system. • Two sources of behavioural biometric data are analyzed. • Eye movements and touch dynamics improve the performance of recognition systems. • Feature-level fusion approach. • The proposed approach allows to obtain an accuracy of over 92%. Web user behavioural recognition is the process by which web users are identified and distinguished through behavioural features. In this work, two sources of behavioural biometric data are analyzed for the development of this web user identification model, touch dynamics and the characteristics extracted from the periocular area related to the pupils, blinks and fixations. The approach adopted used to improve the overall performance of the multimodal biometric recognition system is based on a fusion at the Feature level to which different distance measure techniques (Euclidean, Bray-Curtis, Manhattan, Canberra, Chebyshev, Cosine) are applied to determine if the test sample belongs to the target subject. To further improve the system performance, we have applied multi-data processing methods such as Canonical Correlation Analysis (CCA) and Principal Component Analysis (PCA). The results obtained demonstrate the promise of these two different biometric traits and, above all, of their fusion. In fact, the fusion approach allows obtaining an accuracy higher than that of individual biometrics, reaching an accuracy of over 92%.

Accurate feature extraction for multimodal biometrics combining iris and palmprint

Multimodal biometric systems provide a way to combat with the limitations of a unimodal biometric system which include less accuracy and user acceptability. In this context, a coding based approach called bit-transition code, is proposed for addressing the less-explored problem of designing a biometric-based authentication system by combining the iris and palmprint modalities. The approach is based on the encoding of binary transitions of symmetric and asymmetric parts of the Gabor filtered images at all pixel locations. Score-level fusion is employed to integrate the individual iris and palmprint performances. Experiments are carried out with three benchmark iris/palmprint databases, namely IITD iris and palmprint databases and PolyU palmprint database. The performance is measured in terms of receiver operator characteristics (ROC) curves and other metrics, like equal error rate and area under ROC curves. A comprehensive comparison, with several state-of-the-art approaches, is presented in order to validate the usefulness of the proposed approach.

Enhancing the Accuracy of Multimodal Biometric Systems

: Biometric acknowledgment frameworks have progressed altogether in the most recent decade and their utilization in explicit applications will increment sooner rather than later. The capacity to direct important correlations and evaluations will be urgent to fruitful organization and expanding biometric selection. Indeed, even the best methodology and unimodal biometric frameworks couldn't completely address the issue of exactness and execution as far as their bogus acknowledge rate (FAR) and bogus oddball rate (FRR). In spite of the fact that multimodal biometric frameworks had the option to moderate a portion of the restrictions experienced in unimodal biometric frameworks, like non-all inclusiveness, uniqueness, non-adequacy, loud sensor information, parody assaults, and execution, the issue of low exactness actually continues. In this paper, we survey research papers zeroed in on the precision improvement in data combination of face and finger impression biometric acknowledgment frameworks, decide the primary highlights of the chose techniques, and afterward call attention to their benefits and inadequacies. We propose a novel methodology in relieving the issue of exactness and execution of data combination of multimodal biometric frameworks. This methodology utilizes multilayer perceptron neural organizations in preparing and testing of the organization while additionally proposing the utilization of the most well-known utilized unique mark in biometric field.

A Review on fusion in Multimodal Biometric Spoofing Attack by Different Materials

Biometric gadgets utilize their physiological or behavioural properties for the confirmation and acknowledgment of people. Spoofing attack can be done by using any spoofing materials. Such features can be arranged into unimodal and multimodal frameworks. Some state-of-the-methods have some drawbacks, which reduce the efficiency of the system. Multimodal biometric detecting frameworks utilize at least two behavioural or physiological attributes. The multimodal system has showed to increase the success rate of identification and authentication meaningfully. Data from different modalities are acquired, pre-handled, removed noise, and contrasted and finally converted into features. At last, selection of features acknowledges the identification of a person. In multimodal biometric identification system, biometric features can club at any of the stages. i.e. sensor level, feature level, score level, rank level, and decision level. This paper presents an effective survey on fusion of features at different level in a multimodal biometric system. It also focuses in the field with a better thoughtful of multimodal biometric sensing and handling systems and research inclinations in this field.

VISA: a multimodal database of face and iris traits

In this paper, a new realistic and challenging Face-Iris multimodal biometric database called VISA database is described. One significant problem associated with the development and evaluation of multimodal biometric systems using face and iris biometric traits is the lack of publicly available multimodal databases that are acquired in an unconstrained environment. Currently, there exist no multimodal databases containing a sufficient number of common subjects involved in both face and iris data acquisition process under different conditions. The VISA database fulfills these requirements and it will be a useful tool for the design and development of new algorithms for developing multimodal biometric systems. The VISA iris images are acquired using the IriShield camera. Face images are captured using mobile device. The corpus of a new VISA database consists of face images that vary in expression, pose and illumination, and presence of occlusion whereas iris images vary in illumination, eye movement, and occlusion. A total of more than 5000 images of 100 subjects are collated and used to form the new database. The key features of the VISA dataset are the wide and diverse population of subjects (age and gender). The VISA database is able to support face and/or iris unimodal or multimodal biometric recognition. Hence, the VISA database is a useful addition for the purpose of research and development of biometric systems based on face and iris biometrics. This paper also describes the baseline results of state-of-the-art methods on the VISA dataset and other popular similar datasets. The VISA database will be made available to the public through https://vtu.ac.in/en/visa-multimodal-face-and-iris-biometrics-database/

A feature-level fusion based improved multimodal biometric recognition system using ear and profile face

Ear based identity recognition subject to uncontrolled conditions such as illumination changes, pose variation, low contrast, partial occlusion and noise, is an active research area in the field of biometrics. Meanwhile, multimodal biometrics is becoming increasingly popular and offers improved performance due to the use of multiple sources of information. In this paper, a multimodal biometrics system is proposed based on the ear and profile face that not only alleviates the short-comings of ear biometrics but also improves the overall recognition rate. The ear and profile face modalities are first represented individually using the combination of two efficient local feature descriptors namely, local phase quantization (LPQ) and local directional patterns (LDP). These histogram-based local descriptors are then combined into a high-dimensional feature vector that preserves complementary information in both frequency and spatial domains. The PCA along with the z-score normalization technique is independently applied on each feature vector and the resultant reduced feature vectors are combined at the feature level. The kernel discriminative common vector (KDCV) approach is finally exploited over the combined feature set to derive more discriminative and non-linear features for the identification of individuals using kNN classifier. The effectiveness of the proposed model has been verified with the deep features derived from three popular pre-trained CNN models such as AlexNet, VGG16 and GoogleNet. Experimental results on two benchmark databases clearly show that the proposed approach achieves better performance than individual modality and other state-of-the-art methods.

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme.

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.

Robust multimodal biometric authentication on IoT device through ear shape and arm gesture

Nowadays, authentication is required for both physical access to buildings and internal access to computers and systems. Biometrics are one of the emerging technologies used to protect these highly sensitive structures. However, biometric systems based on a single trait enclose several problems such as noise sensitivity and vulnerability to spoof attacks. In this regard, we present in this paper a fully unobtrusive and robust multimodal authentication system that automatically authenticates a user by the way he/she answers his/her phone, after extracting ear and arm gesture biometric modalities from this single action. To deal the challenges facing ear and arm gesture authentication systems in real-world applications, we propose a new method based on image fragmentation that makes the ear recognition more robust in relation to occlusion. The ear feature extraction process has been made locally using Local Phase Quantization (LPQ) in order to get robustness with respect to pose and illumination variation. We also propose a set of four statistical metrics to extract features from arm gesture signals. The two modalities are combined on score-level using a weighted sum. In order to evaluate our contribution, we conducted a set of experiments to demonstrate the contribution of each of the two biometrics and the advantage of their fusion on the overall performance of the system. The multimodal biometric system achieves an equal error rate (EER) of 5.15%.

Mean-Discrete Algorithm for Individuality Representation

A biometric is one of the common types of pattern recognition, which acquires biometric data from a person. From these data, a feature is established and extracted where these features can be used to identify individual. Exiting works in biometric Identification concentrate on unimodal biometric identification. As such, using features that are uniquely belonging to a person would decrease fraud possibility. Hence, owing to their great accurateness, multimodal biometric systems have become more favored compared with unimodal biometric systems in identification. However, these systems are highly complex. We proposed Mean-Discrete feature-based fusion algorithm for person detection. Its viability and advantage over the unimodal biometric systems are highlighted.

Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications

Biometrics is the technology to identify humans uniquely based on face, iris, and fingerprints, etc. Biometric authentication allows the person recognition automatically on the basis of behavioral or physiological characteristics. Biometrics are broadly employed in several commercial as well as the official identification systems for automatic access control. This paper introduces the model for multimodal biometric recognition based on score level fusion method. The overall procedure of the proposed method involves five steps, such as pre-processing, feature extraction, recognition score using Multi- support vector neural network (Multi-SVNN) for all traits, score level fusion, and recognition using deep belief neural network (DBN). The first step is to input the training images into pre-processing steps. Thus, the pre-processing of three traits, like iris, ear, and finger vein is done. Then, the feature extraction is done for each modality to extract the features. After that, the texture features are extracted from pre-processed images of the ear, iris, and finger vein, and the BiComp features are acquired from individual images using a BiComp mask. Then, the recognition score is computed based on the Multi-SVNN classifier to provide the score individually for all three traits, and the three scores are provided to the DBN. The DBN is trained using the chicken earthworm optimization algorithm (CEWA). The CEWA is the integration of the chicken swarm optimization (CSO), and earthworm optimization algorithm (EWA) for the optimal authentication of the person. The analysis proves that the developed method acquired a maximal accuracy of 95.36%, maximal sensitivity of 95.85%, and specificity of 98.79%, respectively.

A Modified Chaotic Binary Particle Swarm Optimization Scheme and Its Application in Face-Iris Multimodal Biometric Identification

In order to improve the recognition rate of the biometric identification system, the features of each unimodal biometric are often combined in a certain way. However, there are some mutually exclusive redundant features in those combined features, which will degrade the identification performance. To solve this problem, this paper proposes a novel multimodal biometric identification system for face-iris recognition.It is based on binary particle swarm optimization. The face features are extracted by 2D Log-Gabor and Curvelet transform, while iris features are extracted by Curvelet transform. In order to reduce the complexity of the feature-level fusion, we propose a modified chaotic binary particle swarm optimization (MCBPSO) algorithm to select features. It uses kernel extreme learning machine (KELM) as a fitness function and chaotic binary sequences to initialize particle swarms. After the global optimal position (Gbest) is generated in each iteration, the position of Gbest is varied by using chaotic binary sequences, which is useful to realize chaotic local search and avoid falling into the local optimal position. The experiments are conducted on CASIA multimodal iris and face dataset from Chinese Academy of Sciences.The experimental results demonstrate that the proposed system can not only reduce the number of features to one tenth of its original size, but also improve the recognition rate up to 99.78%. Compared with the unimodal iris and face system, the recognition rate of the proposed system are improved by 11.56% and 2% respectively. The experimental results reveal its performance in the verification mode compared with the existing state-of-the-art systems. The proposed system is satisfactory in addressing face-iris multimodal biometric identification.