Multi-biometric templates using fingerprint and voice

As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database

Protection of privacy in biometric data

Biometrics is commonly used in many automated veri cation systems offering several advantages over traditional veri cation methods. Since biometric features are associated with individuals, their leakage will violate individuals\u27 privacy, which can cause serious and continued problems as the biometric data from a person are irreplaceable. To protect the biometric data containing privacy information, a number of privacy-preserving biometric schemes (PPBSs) have been developed over the last decade, but they have various drawbacks. The aim of this paper is to provide a comprehensive overview of the existing PPBSs and give guidance for future privacy-preserving biometric research. In particular, we explain the functional mechanisms of popular PPBSs and present the state-of-the-art privacy-preserving biometric methods based on these mechanisms. Furthermore, we discuss the drawbacks of the existing PPBSs and point out the challenges and future research directions in PPBSs

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Department of Electrical EngineeringBiometrics such as fingerprint, iris, face, and electrocardiogram (ECG) have been investigated as convenient and powerful security tools that can potentially replace or supplement current possession or knowledge based authentication schemes. Recently, multi-spectral skin photomatrix (MSP) has been newly found as one of the biometrics. Moreover, since the interest of usage and security for wearable devices have been increasing, multi-modal biometrics authentication which is combining more than two modalities such as (iris + face) or (iris + fingerprint) for powerful and convenience authentication is widely proposed. However, one practical drawback of biometrics is irrevocability. Unlike password, biometrics can not be canceled and re-used once compromised since they are not changed forever. There have been several works on cancelable biometrics to overcome this drawback. ECG has been investigated as a promising biometrics, but there are few research on cancelable ECG biometrics. As we aim to study a way for multi-modal biometric scheme for wearable devices that is assumed circumstance under some limitations such as relatively high performance, low computing power, and limited information (not sharing users information to the public), in this study, we proposed a multi-modal biometrics authentication by combining ECG and MSP. For investigating the performances versus level of fusions, Adaboost algorithm was studied as a score level fusion method, and Majority Voting was studied as a decision level fusion method. Due to ECG signal is 1 dimensional, it provides benefits in wearable devices for overcoming the computing memory limitation. The reasons that we select MSP combination with ECG are it can be collected by measuring on inner-wrist of human body and it also can be considered as hardly stolen modality in remote ways. For proposed multi-modal biometrics, We evaluate our methods using collected data by Brain-Computer-Interface lab with 63 subjects. Our Adaboost based pro- posed multi modal biometrics method with performance boost yielded 99.7% detection probability at 0.1% false alarm ratio (PD0.1) and 0.3% equal error rate (EER), which are far better than simply combining by Majority Voting algorithm with 21.5% PD0.1 and 1.6% EER. Note that for training the Adaboost algorithm, we used only 9 people dataset which is assumed as public data and not included for testing data set, against for knowledge limitation as the other constraint. As initial step for user template protection, We proposed a cancelable ECG based user authentication using a composite hypothesis testing in compressive sensing do- main by deriving a generalized likelihood ratio test (GLRT) detector. We also pro- posed two performance boost tricks in compressive sensing domain to compensate for performance degradation due to cancelable schemes: user template guided filtering and T-wave shift model based GLRT detector for random projection domain. To verify our proposed method, we investigated cancelable biometrics criteria for the proposed methods to confirm that the proposed algorithms are indeed cancelable. For proposed cancelable ECG authentication, We evaluated our proposed methods using ECG data with 147 subjects from three public ECG data sets (ECG-ID, MIT- BIH Normal / Arrhythmia). Our proposed cancelable ECG authentication method is practically cancelable by satisfying all cancelable biometrics criteria. Moreover, our proposed method with performance boost tricks achieved 97.1% detection probability at 1% false alarm ratio (PD1) and 1.9% equal error rate (EER), which are even better than non-cancelable baseline with 94.4% PD1 and 3.1% EER for single pulse ECG authentication.ope

Biometrics for internet‐of‐things security: A review

The large number of Internet‐of‐Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric‐based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric‐cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state‐of‐the‐art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward‐looking issues and future research directions

Security and accuracy of fingerprint-based biometrics: A review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper

Security and accuracy of fingerprint-based biometrics: A review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper