Current Trends in Multimodal Biometric System—Rank Level Fusion

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.

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.

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.

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

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.

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

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.

In recent years, biometric based security systems achieved more attention due to continuous terrorism threats around the world. However, a security system comprised of a single form of biometric information cannot fulfill users' expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric systems with multiple physiological, behavioral, and soft biometric information are becoming more popular due to increased recognition accuracy. In order to take full advantage of the multimodal approaches, one of the main issues is to implement the fusion mechanism for different biometric information. In this research, we utilize the physiological attributes (face, ear and iris) along with soft biometric information (gender, ethnicity and eye color). A fuzzy fusion mechanism for robust and reliable multimodal biometric based security systems is developed. The proposed fuzzy fusion scheme adopts rank, match score and soft biometrics information as the input and produces final identification decision via a fuzzy rule-based inference system. The experimental results show that the fuzzy fusion method can provide us faster, higher and more reliable recognition performance than conventional unimodal methods. The system can be effectively used at any security critical applications.

A scientific discipline which includes methods that enable identification of people through their behavioral or physical characteristics or sometimes through both is known as Biometrics. Enhancement of security and the increase in its demand has paved way to an interest in an automated method of personal authentication which is based on Biometrics in recent times. Owing to the availability of multiple factors of evidence biometric systems that are multi-modal are considered to be more reliable. In visual interactions among humans, facial recognition is one of the most commonly acceptable biometrics. Selection of features is a challenging task as the interaction among the facial features can be complex. Therefore, Particle Swarm Optimization (PSO) which is a feature selection based on algorithm has been proposed as suitable for the multi-modal biometric system which in turn can bring down significantly the dimension by making use of different fusion schemes and identifying optimal features from among them.

Since unimodal biometric systems based on single source of biometric information are always affected by problems such as noisy sensor data, no-universality and susceptibility to circumvention, using multimodal biometric systems that consolidate evidence from multiple biometric sources can remove some of these problems. In this paper, fingerprint, palm-print and hand-geometry are combined for person identity verification. Unlike other multimodal biometric systems, the user does not have to undergo the inconvenience of using two different sensors as three biometrics can be taken from the same image. Wavelet transform is employed to extract the features from fingerprint and palm-print and hand-geometry feature (such as width, length) is also extracted after the preprocessing phase. We employ feature fusion and mach score fusion together to establish identity. The system was tested on a database of 98 persons. The test performance results indicate the feasibility of the combination.

This paper presents a multimodal biometrics system using watermarking algorithms with two levels of security for simultaneously verifying an individual and protecting the biometric template. Iris template is watermarked in face, such that the face is visible for verification and the watermarked iris is used to cross authenticate the individual and secure the biometrics data as well. The accuracy of the multimodal biometrics system is around 96.8%. This system is also resistant to common attacks on biometric templates.

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.

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.

The traditional watermarking algorithms prove the rightful ownership via embedding of independent watermarks like copyright logos, random noise sequences, text etc into the cover images. Coupling biometrics with watermarking evolved as new and secure approach as it embeds user specific biometric traits and thus, narrows down the vulnerability to impostor attacks. A multimodal biometric watermarking system has been proposed in this paper in the redundant discrete wavelet transform(RDWT). Two biometric traits of the user i.e. the iris and facial features are embedded independently into the sub-bands of the RDWT of cover image taking advantage of its translation invariant property and sufficient embedding capacity. The ownership verification accuracy of the proposed system is tested based on the individual biometric traits as well as the fused trait. The accuracy was enhanced while using the fused score for evaluation. The security of the scheme is strengthened with usage of non-linear chaotic maps, randomization via Hessenberg decomposition, Arnold scrambling and multiple secret keys. The robustness of the scheme has been tested against various attacks and the verification accuracy evaluated based on false acceptance rate, false rejection rate, area under curve and equal error rate to validate the efficacy of the proposed scheme.