Secure Multimodal Biometric Recognition in Principal Component Subspace

In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

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.

Biometric systems based solely on one-modal biometrics are often not able to meet the desired performance requirements for large user population applications, due to problems such as noisy data, intra-class variations, restricted degrees of freedom, nonuniversity, spoof attacks, and unacceptable error rates. Multimodal biometrics refers to the use of a combination of two or more biometric modalities in a single identification system. The most compelling reason to combine different modalities is to improve the recognition accuracy. This can be done when features of different biometrics are statistically independent. This paper overviews and discusses the various scenarios that are possible in multimodal biometric systems using fingerprint, face and iris recognition, the levels of fusion that are possible and the integration strategies that can be adopted to fuse information and improve overall system accuracy. This paper also discusses how the image quality of fingerprint, face and iris used in the multimodal biometric systems affects the overall identification accuracy and the need of staffing for the secondary human validation. For a large user population identification system, which often has more than tens or hundreds of millions of subject images already enrolled in the matcher databases and has to process more than hundreds of thousands of identification requests, the system's identification accuracy and the need of staffing levels to properly operate the system are two of the most important factors in determining whether a system is properly designed and integrated

A biometric system is an amalgamation of algorithms that uses an individual’s unique biological data innate physical features (fingerprints, retina, face, ears, etc.) or behavioural features (signature, voice, keystroke dynamics, etc.) or both to authenticate the individual. This essentially makes a biometric system function as a pattern recognition system that helps an individual in any kind of adversarial environment. Hence, like any conventional authentication system, it is vulnerable to the attacks of malicious adversaries that try to manipulate the data and thus decline the security of the system by compromising its robustness. These possible attacks have not been considered while developing most biometric authentication systems. Our work describes multimodal biometrics, its feature extraction methods, research challenges and focuses on the major security issues in both unimodal and multimodal biometric systems. This paper summarizes some robust multimodal systems, their countermeasures and algorithms that have been devised to tackle these security challenges.KeywordsBiometricsMultimodal biometricsBiometric fusionSecurityAttacksCountermeasuresEncryption

Biometric identification referes to idetifying an individual based on his or her physilogical or beavioral characteristics. The use of more than one biometric identfiers in a biometric system, called the multimodal biometric system, increases the overall system accuracy and hence increase security, as well as reduce the enrollment problems. An effective and appropriate fusion strategy is needed to integrate different biometric information in such multimodal systems. This chapter provides an in-depth overview of traditional multimodal biometric systems and current trends in multimodal biometric fusion. Various approaches of rank level fusion, which is an not heavily investigated by researchers yet, are also illustrated in details in this chapter. Pros and cons of these rank fusion approaches are discussed which can be helpful for large scale multimodal biometric system deployment.

Biometric authentication is the process of recognizing a person by means of his\her psychological or behavioral traits. One of the most important issues faced by the biometric system developer is to protect the template obtained from the biometric of a person. Unimodal biometric system has some drawbacks such as noisy data, interclass variations and spoof attack. Multimodal biometric system has been developed to address the boundaries of unimodal biometric system and increase the security of template. In this paper, template security analysis of multimodal biometric system based of fingerprint and palmprint is proposed and implemented. Fuzzy vault scheme is employed to protect both the fingerprint and palmprint template. At enrollment, image processing techniques such as image enhancement, segmentation and bottom-hat filtering are applied on both the biometric to improve the quality and subsequently the most important features are extracted. Extracted features are concatenated. Combined features along with secret key are utilized to generate the database in the vault. During authentication, query images are sent as an input with the stored template to recover the key. Experimental results are shown that the proposed multi biometrics system performs well than the other methods considered for comparison.

Recent works have shown that multi-modal biometric systems are not robust against spoofing attacks [12, 15, 13]. However, this conclusion has been obtained under the hypothesis of a “worst case” attack, where the attacker is able to replicate perfectly the genuine biometric traits. Aim of this paper is to analyse the robustness of some multi-modal verification systems, combining fingerprint and face biometrics, under realistic spoofing attacks, in order to investigate the validity of the results obtained under the worst-case attack assumption.

Unimodal biometric systems often face significant limitations due to sensitivity to noise intraclass variability and other factors. Multimodal biometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. However, the multimodal biometric system is limited to the time constraints due to its multiple processing stages. To overcome these limitations this paper presents a fast multimodal verification system by using the dynamic regions of the fingerprint image and enhanced iris segmentation method. The multimodal system is fused using rank level fusion at the verification stage. The performance of the biometric system shows improvement in the False Acceptance Rate (FAR) and Equal Error Rate (EER) curves. Furthermore, the time taken for the training and verification phase has a reduction of 15% when compared with the existing system tested on the slow processing mobile devices.

Biometric authentication systems operating in real world environments using a single modality are found to be insecure and unreliable due to numerous limitations. Multimodal biometric systems have better accuracy and reliability due to the use of multiple biometric traits to authenticate a claimed identity or perform identification. In this paper a novel method for person identification using multimodal biometrics with hand geometry and palmprint biometric traits is proposed. The geometrical information embedded in the user hand and palmprint images are brought out through the graph representations. The topological characterization of the image moments, represented as the virtual nodes of the palmprint image graph is a novel feature of this work. The user hand and palmprint images are represented as weighted undirected graphs and spectral characteristics of the graphs are extracted as features vectors. The feature vectors of the hand geometry and palmprint are fused at feature level to obtain a graph spectral feature vector to represent the person. User identification is performed by using a multiclass support vector machine (SVM) classifier. The experimental results demonstrate, an appreciable performance giving identification rate of 99.19% for multimodal biometric after feature level fusion of hand geometry and palmprint modalities. The performance is investigated by conducting the experiments separately for handgeometry, palmprint and fused feature vectors for person identification. Experimental results show that the proposed multimodal system achieves better performance than the unimodal cues, and can be used in high security applications. Further comparison show that it is better than similar other multimodal techniques.

In this paper, a new transform Gabor filtered Wigner transform (GFWT) has been proposed. In GFWT, Gabor filtering is performed on the Wigner transformed image. Wigner transform gives a simultaneous representation of an image in time and frequency domain which is further processed using Gabor filters. The proposed transform is then used to extract the features from the biometrics to develop different multimodal biometric systems. A detailed study has been carried out in which, different unimodal and multimodal systems such as feature level and score level fusion are analysed. In order to improve the performance of the system, an optimisation technique, particle swarm optimisation (PSO) is used to find the optimal parameters of the Gabor filter and to select the significant GFWT feature vector. The PSO technique not only improves the performance of the system but also able to reduce the dimension of the feature vectors. Numerical experiments are carried out on face and palmprint database to show the effectiveness of the proposed transform for different unimodal and multimodal systems.

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the ”Secure Sense” framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities.

The primary factor driving the growing interest in novel biometric features is the vulnerability of traditional methods like fingerprint and facial recognition to forgery. This study focuses on a multimodal biometric identification system that integrates data from electroencephalograms (EEG) and facial features. To derive valuable insights from EEG data, we apply signal processing techniques such as filtering, segmentation, and feature extraction, alongside Daubechies-4 (DB4) wavelet analysis with five decomposition levels. The enhanced facial video features include entropy calculations and tracking of facial measurements. Six classifiers Gaussian Naïve Bayes, K-Nearest Neighbour, Random Forest, AdaBoost, Support Vector Machine, and Multilayer Perceptron were trained utilizing the combined EEG and facial data. Findings reveal that AdaBoost and Random Forest emerged as the most effective classifiers for this application, achieving accuracies of 99.87±0.13% and requiring EEG recording times of 2s and 1.5s, respectively, showcasing excellent precision.

The interface of computer technologies and biology is having a huge impact on society. Human recognition technology research projects promises new life to many security consulting firms and personal identification system manufacturers. The physiological or biological features of an individual, called biometrics, are unique to each human and remain unaltered during a person's lifetime and give a promising solution for security management. This paper presents an intelligent multimodal biometric verification system for physical access control, based on fusion of iris, face and fingerprint patterns. Feature vectors are created independently for query images and are then compared with the enrolled templates of each biometric trait to compute the matching score. The final decision is made by fusion at this matching score level. The proposed system is designed to suit embedded solutions for high security access in pervasive environments using biometric features.

A multi-modal biometric authentication system based on iterative fusion operator trees is considered, where the per-user performance is also depending on the order of biometric feature evaluations. Therefore, each user would have its own optimal choice of order parameters of the biometric system. However, for a system approach there should be only one general parametrization of the whole biometric system for all users, and the necessary consequence are performance wins and losses for each user. The common approach for this purpose is to use the parameter setting that maximizes the average performance over users. However, this does not necessarily reflect a good balance of performance wins and losses. In order to gain more insight into the corresponding trade-offs, and to facilitate an effective selection of an order parameter, the concept of fairness relations and their maximality, which has become a recent topic of many investigations in communication network resource sharing tasks, is applied to this problem of multi-modal biometric systems. The central idea of the paper is that by applying fairness relation to select the parameters, we obtain a system where subjects have a more fair chance of being right authenticated. A comparative study of an experimental test data set shows that maxmin fairness gives solutions with major drawbacks for the majority of users, while proportional fairness comes close to the maximum average.

A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint