Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Conclusions <s> In this paper, we aim to shed some light on the benefits and challenges brought about by using biometrics for securing mobile payments. Some potential solutions to address the challenges are also proposed and analyzed. Based on our analysis, it is shown that biometric cryptosystems are the suitable choice for providing security protection to biometric templates and enabling a seamless integration with the existing password-based payment systems. Moreover, the employment of stable feature sets or multimodal biometrics is able to improve the recognition accuracy of biometric-based mobile payment systems. Finally, to provide security for mobile payment systems, we propose a secure mobile payment infrastructure which combines a biometric cryptography modal with a time-synchronized one-time password (TOTP) encryption modal. <s> BIB001 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Conclusions <s> Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, whereas the second approach focuses on learning the weights of the network via back propagation. We consider nine biometric spoofing benchmarks—each one containing real and fake samples of a given biometric modality and attack type—and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come. <s> BIB002 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Conclusions <s> Biometric systems detect authenticity based on users' distinct physiological or behavioral characteristics for purposes of identification and access control. These pattern recognition systems are difficult to bypass when compared to traditional token or password based systems. This paper is proposing a new deep learning architecture for fingerprint recognition. The proposed architecture comprises of a pre-processing stage for extracting texture features from fingerprints, and this stage is performed by using histogram equalization, Gabor enhancement and fingerprint thinning. The pre-processed fingerprints are input into a Deep Convolutional Neural Network classifier. The proposed approach has achieved 98.21% classification accuracy with 0.9 loss. The obtained accuracy is significantly higher than previously reported results on the same dataset, 77%. <s> BIB003
This paper gives a comprehensive review of two significant (and competing) measures for fingerprint-based biometric systems; that is, security and recognition accuracy. In regards to security, we have analyzed two categories of attacks: attacks to user interface and attacks to template databases. Countermeasures to defend against these attacks are also discussed. A total of 42 research articles in the area of biometric security (8 in liveness detection, 18 in cancelable biometrics, and 16 in biometric cryptography) are reviewed and discussed. In regards to recognition accuracy, in our opinion, although remarkable recognition accuracy has been attained, matching performance can still be unsatisfactory under some non-ideal conditions (e.g., latent fingerprint matching) or when the system security level is high. Since the requirements of system security impact recognition accuracy, it calls for the biometric system designers to carefully consider how to strike a good balance between recognition accuracy and security. In view of the above issues, some latest research outcomes are analyzed and summarized in this paper. Despite the improvement in recognition accuracy under non-ideal conditions and recent advances in biometric template protection, a number of open issues still exist, which call upon biometric researchers to resolve them. We highlight some research challenges and future directions in the following: i. New developments in deep learning techniques have enhanced the performance of biometric systems across a wide range of biometric modalities, such as face recognition modality. We envisage that deep learning techniques BIB002  BIB003 will also be potential tools for latent fingerprint matching. However, the use of deep learning algorithms may bring potential threats to biometric systems because of the vulnerabilities of those deep learning algorithms themselves. ii. The security issues (e.g., spoofing attacks, attacks to biometric templates) analyzed for a general biometric system are also valid to any biometric system on different platforms, for example, a mobile platform. Nowadays, smartphones are becoming more and more popular, thus forming a promising platform for the use of biometrics BIB001 . However, mobile biometrics face more challenges, since smartphones usually have less computing capability and limited energy. Therefore, light-weight secure algorithm design for mobile biometrics is an emerging research topic  [83] [84] . iii. Trade-off between security and recognition accuracy in fingerprint template protection remains a challenge. As shown in Table 5 , the best matching performance of fingerprint competition with template protection is the EER = 1.542%, which is much worse than that (EER = 0.022%) without template protection. Besides exploring more robust and distinctive features and designing better transformation functions, the use of multi-biometrics in template protection design is likely to be the way forward and deserves further research.