Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Accuracy Without vs. With Template Protection <s> Biometric recognition is an integral component of modern identity management and access control systems. Due to the strong and permanent link between individuals and their biometric traits, exposure of enrolled users? biometric information to adversaries can seriously compromise biometric system security and user privacy. Numerous techniques have been proposed for biometric template protection over the last 20 years. While these techniques are theoretically sound, they seldom guarantee the desired noninvertibility, revocability, and nonlinkability properties without significantly degrading the recognition performance. The objective of this work is to analyze the factors contributing to this performance divide and highlight promising research directions to bridge this gap. The design of invariant biometric representations remains a fundamental problem, despite recent attempts to address this issue through feature adaptation schemes. The difficulty in estimating the statistical distribution of biometric features not only hinders the development of better template protection algorithms but also diminishes the ability to quantify the noninvertibility and nonlinkability of existing algorithms. Finally, achieving nonlinkability without the use of external secrets (e.g., passwords) continues to be a challenging proposition. Further research on the above issues is required to cross the chasm between theory and practice in biometric ?template protection. <s> BIB001 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Accuracy Without vs. With Template Protection <s> Fingerprint and finger-vein based cancelable multi-biometric template design.Flexible feature-level fusion strategy with three fusion options.Enhanced partial discrete Fourier transform based non-invertible transformation.High-performing cancelable multi-biometric templates with strong security. Compared to uni-biometric systems, multi-biometric systems, which fuse multiple biometric features, can improve recognition accuracy and security. However, due to the challenging issues such as feature fusion and biometric template security, there is little research on cancelable multi-biometric systems. In this paper, we propose a fingerprint and finger-vein based cancelable multi-biometric system, which provides template protection and revocability. The proposed multi-biometric system combines the minutia-based fingerprint feature set and image-based finger-vein feature set. We develop a feature-level fusion strategy with three fusion options. Matching performance and security strength using these different fusion options are thoroughly evaluated and analyzed. Moreover, compared with the original partial discrete Fourier transform (P-DFT), security of the proposed multi-biometric system is strengthened, thanks to the enhanced partial discrete Fourier transform (EP-DFT) based non-invertible transformation. <s> BIB002 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Accuracy Without vs. With Template Protection <s> Physical access control is an indispensable component of a critical infrastructure. Traditional password-based methods for access control used in the critical infrastructure security systems have limitations. With the advance of new biometric recognition technologies, security control for critical infrastructures can be improved by the use of biometrics. In this paper, we propose an enhanced cancelable biometric system, which contains two layers, a core layer and an expendable layer, to provide reliable access control for critical infrastructures. The core layer applies random projection-based non-invertible transformation to the fingerprint feature set, so as to provide template protection and revocability. The expendable layer is used to protect the transformation key, which is the main weakness contributing to attacks via record multiplicity. This improvement enhances the overall system security, and undoubtedly, this extra security is an advantage over the existing cancelable biometric systems. <s> BIB003
Template protection techniques provide safeguards to biometric templates and the protected template should leak as little information of the original template as possible BIB001 . In biometric cryptosystems, information of reference points can help to enhance the recognition accuracy but will leak important information about the original template, and thus it should not be made public. In cancelable biometrics, random projection based transformation is a typical many-to-one mapping, in which the dimension of the original template is reduced. Because less information of the original template is kept, a lower-dimensional transformed template is more secure. However, with less information of the original template preserved, it might result in accuracy degradation BIB002 BIB003 . Therefore, there is a balance between recognition accuracy and security.  Template protection techniques provide safeguards to biometric templates and the protected template should leak as little information of the original template as possible BIB001 . In biometric cryptosystems, information of reference points can help to enhance the recognition accuracy but will leak important information about the original template, and thus it should not be made public. In cancelable biometrics, random projection based transformation is a typical many-to-one mapping, in which the dimension of the original template is reduced. Because less information of the original template is kept, a lower-dimensional transformed template is more secure. However, with less information of the original template preserved, it might result in accuracy degradation BIB002 BIB003 . Therefore, there is a balance between recognition accuracy and security. The best three fingerprint matching results with and without template protection in the FVC-ongoing Competition, listed in Table 5 , show that matching accuracy with template protection is much worse than that without template protection. Also, recognition accuracy of some existing systems in literature with low/no level of security versus others with high level of security is reported in Table 6 for comparison. From Table 6 , we can see that with the same or similar FAR, when the security level is high, the FRR becomes worse. For example, for database FVC2002DB1, results in  show that when FAR is 0, FRR is 3.18% with the original feature, whereas FRR increases to 51.29% when the highest security level is set to protect the original feature. Some recently published methods do not give recognition results on low/no level of security. Therefore, their recognition performance in the "Low/No security" column is indicated by "-". In this section, recognition accuracy, as the other important measure in biometric system design, is discussed and analyzed. From our thorough analysis, it can be seen that recognition accuracy of most existing biometric systems, either with or without template protection, are tested in ideal conditions, which are far from real-life scenarios, where the obtained images (e.g., latent fingerprint) are of extremely low quality. Also, the recognition accuracy of the systems with template protection is lower than that without template protection. The main reason is the information loss in the process of feature adaptation, which converts original features into another format to satisfy the matching metrics for transformed templates, e.g., hamming distance for fuzzy commitment and set difference for fuzzy vault. Therefore, more study needs to be put into the design of stable features and suitable feature adaptation methods, so as to minimize information loss.