Multimodal Biometrics Research Articles

Different Approaches for Face Authentication as Part of a Multimodal Biometrics System

This paper describes different approaches for the face authentication from the features and classification abilities point of view. Authors compare two types of features - Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) including their combination. These parameters are classified using Multilayer Neural Network (MLNN) and Support Vector Machines (SVM). Face authentication consists of several steps. The first step contains Viola-Jones algorithm for face detection. Authors resize the detected face for a fixed vector and afterwards, it is converted into grayscale. Next, feature extraction with a simple Min-Max normalization is applied. Obtained parameters are evaluated by classifiers and for each detected face, authors get posterior probability as the output of the classifier. Different approaches for face authentication are compared with each other using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) curves. The results are verified with AR Face Database and elaborated in a feature extraction and classifier design point of view. Best results were achieved by HOG feature for SVM classifier. Detailed results are listed in the text below.

Performance Evaluation of User Independent Score Normalization Based Quadratic Function in Multimodal Biometric

Normalization is an essential step in multimodal biometric system that involves various nature and scale of outputs from different modalities before employing any fusion techniques. This paper proposes score normalization technique based on mapping function to increase the separation of score at overlap region and reduce the effect of overlap region on fusion algorithm. The effect of the proposed normalization technique on recognition system performance for different fusion methods is examined. Experiments on three different NIST databases suggest that integrating the proposed normalization technique with the classical simple rule fusion strategies (sum, min and max) and SVM-based fusion results significant improvement compared to other baseline normalization techniques used in this work.

Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

Human recognition in a multi-biometric system is performed by combining biometric clues from different sources (multiple sensors, units, algorithms, samples and modalities) at different levels (sensor, feature, score, rank and decision level). Low computational complexity and adequate data for fusion make the score-level fusion a preferable option over other levels of fusion. However, incompatibility issue prevails at this level as scores obtained from different uni-biometric systems are disparate in nature. This disparity can be resolved by using score normalisation before fusion. This study first analysed the effect of generalised extreme value distribution-based score normalisation technique on different fusion techniques and then proposes an efficient score fusion technique based on Dezert-Smarandache theory (DSmT). A unique blend of belief assignment and decision-making methods in the DSmT framework is proposed for score-level fusion. For evaluation of the proposed method, experiments are performed on multi-algorithm, multi-unit and multi-modal biometrics systems created from three publicly available datasets: (i) NIST BSSR1 multi-biometric score database, (ii) face recognition grand challenge (FRGC) V2.0 and (iii) LG4000 iris dataset. Comparative studies of performance analysis show the efficiency of the authors' proposed method over other recently published state-of-the-art fusion methods.

Kinect User Authentication using Multimodal Biometric

Objectives: Facial Recognition system has been the most popular non-intrusive authentication system. The proposed system offers to improve the accuracy of facial recognition using multimodal authentication with a 3d depth sensor. Methods/Statistical Analysis: To increase the reliability and robust of the system, we superimpose skeletal biometric features with facial features where data combined using 3D Imaging Technology. Statistical approaches are used for feature transformation using PCA and LDA Algorithm. Findings: To evaluate the system, we collected nearly 1000 individuals with two samples for each, captured in indoor environments under various light effects. The accuracy of the system is built with 95% confidence interval and based on a classifier, three performance curves Receiver Operating Characteristics (ROC), Cumulative Match Score Curves (CMC) and Expected Performance Curves (EPC) are used for performance evaluation. The FRR of the proposed multimodal fusion approach is 0.12 at 0.001 minimal FAR with 95% accuracy. Applications/Improvements: Multimodal biometric is required for strong authentication for the surveillance of smart home, cities and health care sectors where security plays a vital role. This fusion technique improves the reliability, latency, and accuracy of the system in recognizing and authenticating the user. Keywords: 3D Imaging Technology, Face Features, Multimodal Authentication, Skeletal Biometric

Efficient storage and retrieval of medical records using fusion-based multimodal biometrics

Biometrics helps to uniquely identify a person using their biological features and hence is used to develop systems with a high level of security. Multimodal biometrics further increases the level of security and provides controlled access, using the combination of multiple traits to identify a person. This paper presents an application of multimodal biometrics to efficiently store, access and retrieve medical records of a patient, which is independent of the hospital servers across the country. In case of emergencies, where it is important to know the medical history of a patient, the record can securely be accessed from a cloud server by using their biometric traits. Here we use two traits, face print and fingerprint to simulate the process of uniquely identifying a patient's record which is stored on a cloud server. The records can only be accessed by authorised representatives of the hospitals, which preserve its confidentiality. Feature level fusion technique is used to determine if a record, corresponding to a patient is available in the database. Cryptographic methods like shuffling algorithms are applied for further security of the records.

An intelligent technique for uniquely recognising face and finger image using learning vector quantisation (LVQ)-based template key generation

In current days, to identify, verify and detect the humans by using the bio recognition based on multimodal biometric was speedily developed which focus to necessitate the security keys in the authentication process of industries/organisation. The main goal of this organisation system is to integrate the instability biometric feature of the users and it is also used to formulate the key unimaginable to an unauthorised person. In this proposed work, in multimodal biometrics methods, particularly face and fingerprint based novel pattern matching is described. There are three modules to be used for exact feature extraction; multimodal biometric pattern generation and pattern matching are used to achieve better result for this proposed work. At first, from the face images and finger prints the minutiae points, face features and some other features are extracted. Consequently, by using a density-based score level fusion, the extracted features are merged together at match score level to create the multi-biometric pattern. After that, learning vector quantisation is used to perform the pattern matching. The assessment process was performed using large scale subjects to enter the system proving the ability of the proposed system over the state of art.

Efficient storage and retrieval of medical records using fusion-based multimodal biometrics

Biometrics helps to uniquely identify a person using their biological features and hence is used to develop systems with a high level of security. Multimodal biometrics further increases the level of security and provides controlled access, using the combination of multiple traits to identify a person. This paper presents an application of multimodal biometrics to efficiently store, access and retrieve medical records of a patient, which is independent of the hospital servers across the country. In case of emergencies, where it is important to know the medical history of a patient, the record can securely be accessed from a cloud server by using their biometric traits. Here we use two traits, face print and fingerprint to simulate the process of uniquely identifying a patient's record which is stored on a cloud server. The records can only be accessed by authorised representatives of the hospitals, which preserve its confidentiality. Feature level fusion technique is used to determine if a record, corresponding to a patient is available in the database. Cryptographic methods like shuffling algorithms are applied for further security of the records.

Biometric security system: a rigorous review of unimodal and multimodal biometrics techniques

Biometric-based system is used for authentication of an individual and to counter the possible threats used for security purpose. A wide variety of systems require reliable personal authentication schemes to either confirm or determine the identity of individuals requesting their services. Typical scenarios are access control and authentication transaction. Examples of such systems are automatic teller machines (ATMs), criminal verification, national unique identifications, border crossing, airports, cellular phones, etc. In the absence of robust authentication schemes, these systems are vulnerable to the wiles of an impostor. The purpose of such schemes is to ensure that the rendered services are accessed by the legitimate user, and not anyone else. Many researchers developed biometric-based system despite that each system has its own limitations. The main aim of this paper is to give a qualitative and computational analysis of existing biometric-based system and describes various unimodal and multimodal systems.

Multimodal biometrics Fusion based on TER and Hybrid Intelligent Multiple Hidden Layer Probabilistic Extreme Learning Machine

In this paper, a novel fusion method based on Total Error Rate (TER) and multiple hidden layer probabilistic extreme learning machine is proposed. At first, the study transfers the matching scores into TER based on corresponding False Reject Rates (FRR) and False Accept Rates (FAR) aims at avoiding to calculating the posterior probability. At the second, a new fusion strategy based on multiple hidden layer probabilistic extreme learning machine is introduced, which optimizes the architecture of hidden nodes by weighted calculation of different output matrices and then transforms the numeric output of ELM to the probabilistic outputs and unifies the outputs in a fixed range, the matrices weights and the output weights are optimized using a hybrid intelligent algorithm based on differential evolution and particle swarm optimization. Experiment result shown that the proposed method renders very good performance as it is quite computationally and outperforms the traditional score level fusion schemes, the experimental result also confirms the effectiveness of the proposed method to improve the performance of multibiometric system.

Biometric security system: a rigorous review of unimodal and multimodal biometrics techniques

Biometric security system: a rigorous review of unimodal and multimodal biometrics techniques

Multimodal biometric hashkey cryptography based authentication and encryption for advanced security in cloud

Cloud Computing (CC) is a technology that is growing at a faster pace, gaining much popularity with startup companies increasing and opting for clouds’ services. The growth rate of this technology is high and so is the security threat. Though there are existing authentication systems like two factor authentication, biometric based authentication and so on, hackers still pose greater challenges. Though password-based authentication system is simple, it is highly vulnerable to security breaches. Biometric based authentication has better security, but usage of single biometric traits can also be easily compromised. Thus, this research work, Multimodal Biometric Hashkey Cryptography (MBHC) brings about a novel security framework that focuses on both authentication and securing cloud data using multiple modalities. Authentication is based on multiple biometric modalities viz. Fingerprint, Iris and Face features. Features from these modalities are extracted using linear filters. Artificial Fish Swarm Algorithm (AFSA) is used for feature optimization, further to which Support Vector Machine (SVM) acts as classifier for identifying genuine users against imposters. Cryptographic keys are generated from these modalities that serve as input to Advanced Encryption Standard (AES) Algorithm for encrypting and decrypting the information stored in cloud. Integrity of data is also ensured using hash function for which the facial features and the encrypted message are the inputs. Thus, the experimental results show that the proposed architecture provides improved security for cloud computing environment when compared to the existing models.

Multiple classifiers in biometrics. part 1: Fundamentals and review

We provide an introduction to Multiple Classifier Systems (MCS) including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods. This introduction complements other existing overviews of MCS, as here we also review the most prevalent theoretical framework for MCS and discuss theoretical developments related to MCS.The introduction to MCS is then followed by a review of the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. This review includes general descriptions of successful MCS methods and architectures in order to facilitate the export of them to other information fusion problems.Based on the theory and framework introduced here, in the companion paper we then develop in more technical detail recent trends and developments in MCS from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in the present paper, methods in the companion paper are introduced in a general way so they can be applied to other information fusion problems as well. Finally, also in the companion paper, we discuss open challenges in biometrics and the role of MCS to advance them.

Multiple classifiers in biometrics. Part 2: Trends and challenges

The present paper is Part 2 in this series of two papers. In Part 1 we provided an introduction to Multiple Classifier Systems (MCS) with a focus into the fundamentals: basic nomenclature, key elements, architecture, main methods, and prevalent theory and framework. Part 1 then overviewed the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. Here in Part 2 we present in more technical detail recent trends and developments in MCS coming from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in Part 1, methods here are described in a general way so they can be applied to other information fusion problems as well. Finally, we also discuss here open challenges in biometrics in which MCS can play a key role.

A secure Multimodal Biometric Authentication with Cryptographic key Management Using Double Random Phase Encoding

A secure Multimodal Biometric Authentication with Cryptographic key Management Using Double Random Phase Encoding

Security Management of Authentication System based on Multimodal Biometric Features

Security Management of Authentication System based on Multimodal Biometric Features

Multimodal Biometric Authentication Algorithm Using Iris, Palm Print, Face and Signature with Encoded DWT

A multimodal biometric system is applied to recognize individuals as authentication, identification and verification for claimed identity. Multimodal biometrics increases the security level accuracy, spoof of attacks, noise in collected data, intra-class variations, inter-class variations, non universality etc. In this paper a multi modal biometric algorithm is designed by integrating iris, palm print, face and signature based on encoded discrete wavelet transform for image analysis and authentication. Multi level wavelet based fusion approach is applied, integrated and encoded into single composite image for matching decision. It reduces the memory size, increases the recognition accuracy and ERR using multimodal biometric approach when compared to individual biometric traits. The complexity of fusion and the reconstruction algorithm is suitable for many real time applications.

Development of Streaming Audio and Video System for Multimodal Biometric Security System

Development of Streaming Audio and Video System for Multimodal Biometric Security System

A Survey on Different Levels of Fusion in Multimodal Biometrics

Objectives: In this paper, we have compiled a survey which is based on the comparison of various fusion techniques which are most commonly used in a multimodal biometric system. Methods/Statistical Analysis: The Multimodal Biometric systems are providing identification and human security over last few decades. We have also discussed various techniques used in different level of fusion with the objective of improving performance and robustness at each level of fusion. Findings: The extensive study shows that almost perfect accuracy rates can be achieved by multimodal fusion based protection processes. A comparison has also been made depending upon fusion levels, acceptance rates and accuracy levels. It is concluded from the fact that a combination of two identities when fused together, can reach very high accuracy levels. Applications: The survey given in this paper will help researchers in understanding levels of fusion in multimodal biometrics. Keywords: Accuracy, Fusion Level, Fusion Techniques, Multimodal Biometric System

Design and Modeling of Real Time Multimodal Biometric Authentication System

Authentication is a major challenge for accessing different web-based applications, information exchange, communicating people, conducting e-business and performing financial operations, access to system like ATM, Wi-Fi etc. Biometrics based authentication could be used to prevent unauthorized access to these systems. Multi-modal biometric authentication system could be used to further strengthen the security by overcoming the limitations imposed by unimodal biometric authentication system. This paper aimed to design and model the multimodal biometric authentication system for secured access to different web applications through WLAN. The proposed system is designed using UML (Unified Modelling Language) diagrams.

A Multimodal Biometric Authentication Technique using Fused Features of Finger, Palm and Speech

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.