Robustness of multi-modal biometric verification systems under realistic spoofing attacks

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.

In this paper, we first discussed the rationale behind the attacks on biometric authentication systems. We also discussed the eight conceptual points of vulnerability that an attacker can use to bypass a biometric recognition system. Among all the attacks, the spoofing attacks (providing false biometric features for the sensor) gained much attention and therefore we have defined and discussed them in detail. Indeed, we will present thorough review of the state of the art of usurpation methods of both faces a biometric digital fingerprint. Then, we will give the review of the literature on the evaluation of the robustness of multimodal biometric systems against spoofing attacks. Various existing studies concluded the popular thought that the multimodal biometric systems are vulnerable to spoofing attacks and can be cracked even by usurping a single feature.

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

Biometrics is an ever-emerging field which is now being utilized in many applications and systems. Face biometrics are quite prevalent and have already been deployed in many devices and applications. Iris biometrics though already studied and deployed in certain areas are considered very effective due to their lesser chances of changing with time and uniqueness. Face detection and iris detection for verification have been employed in various biometric applications already. Unimodal systems of face and iris are secure, but for enhanced levels of security, multimodal biometric systems are being created. Multimodal biometric systems are those systems which use the various biometric traits simultaneously, in order to authenticate a person’s identity. There is a direct enhancement in the verification accuracy due to such systems. The fusion of these two biometrics in consideration to make the process of unauthenticated or unauthorized intervention complex because it will be difficult and inefficient to get multiple biometric traits simultaneously in real time. In order to draw a contrast between a unimodal approach and multimodal approach for the biometrics, this paper presents a survey on using individual face classifiers or individual iris classifiers and compares it with the combined classifier results. After analysing the various techniques, it is concluded with the directions towards the future scope.

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.

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.

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.

Review Paper on Applications of Principal Component Analysis in Multimodal Biometrics System

Recent works have shown that multimodal biometric systems can be evaded by spoofing only a single biometric trait. In this paper, we propose a method to evaluate the robustness of such systems against spoofing attacks, when score-level fusion rules are used. The aim is to rank several score-level fusion rules, to allow the designer to choose the most robust one according to the model predictions. Our method does not require to fabricate fake biometric traits, and allows one to simulate different possible spoofing attacks using the information of genuine and impostor distributions. Reported results, using data set containing realistic spoofing attacks, show that our method can rank correctly score-level fusion rules under spoofing attacks.

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.

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

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.

Multi-modal biometric verification systems are in active development and show impressive performance nowadays. However, such systems need additional protection from spoofing attacks. In our paper we present full pipeline of anti-spoofing method (based on our previous work) for bimodal audiovisual verification system. This method allows to evaluate parameters of quality for a sequence of face images during a verification process. Based on this parameters it’s decided whether the data is suitable for processing by the standard method (fiducial points based audiovisual liveness detection, FALD). If the quality of data is not sufficient, then our system switches to a new algorithm (svm-based audiovisual liveness detection, SALD), which provides less protection quality, but is able to operate when FALD is unsuitable. To improve the quality of the FALD algorithm we have collected the special dataset. This dataset allows to get better reliability of the algorithm for searching of fiducial points on the user’s face image. Tests show that developed system can significantly improve the quality of anti-spoofing protection versus our previous work.

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 new privilege of biometrics help to reduce the stress of user, Which comes along with the traditional access methods of passwords and token. Using the biometrics limitations and weaknesses can be knocked out. However, biometrics has raise privacy risks and new security since they cannot be easily revoked. Due to the spoofing attack on biometrics. Thus, to protect biometric traits against spoofing attack a multimodal biometric jammer scheme for the security enhancement have been developed and suggested in this paper. Firstly, we analyze why the multimodal biometric system have attracted attention for high security-demanding schemes. Secondly, security of biometric system is increasing and prevented it from spoofing attack developing a machine learning system model. We show that these machine learning algorithms perform pre-processing of biometric traits images. Further we analyze user identification with the increase precision and reliability using biometric features. Where feature extraction of each one trait of biometric is done and then all features are concatenation to get a single feature. With the aid of machine learning classifier using extracted features the algorithm predict the result of the system.