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.

Most existing biometric systems designed for healthcare applications only use biometrics for authentication/access control without considering its other function - data encryption. In this paper, we propose a cancelable finger-vein based bio-cryptosystem, which not only can provide authentication but also can encrypt sensitive healthcare data through a biometric cryptographic technique, fuzzy commitment scheme (FCS). The proposed bio-cryptosystem stores both the encrypted version of healthcare data and the biometric template on a smart card for the reason that it is safer if the biometric data never leaves the card. The employment of the cancelable biometrics further enhances system security. The experimental results and security analysis show the validity of the proposed scheme.

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. To address the issue, in this paper we develop a novel biometric template protection algorithm using the binary decision diagram (BDD), which is stacked with an artificial neural network - the multi-layer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.

Any proposal to provide security for Implantable Medical Devices (IMDs), such as cardiac pacemakers and defibrillators, has to achieve a trade-off between security and accessibility for doctors to gain access to an IMD, especially in an emergency scenario. In this paper, we propose a Finger-to-Heart (F2H) IMD authentication scheme to address this trade-off between security and accessibility. This scheme utilizes a patient's fingerprint to perform authentication for gaining access to the IMD. Doctors can gain access to the IMD and perform emergency treatment by scanning the patient's finger tip instead of asking the patient for passwords/security tokens thereby achieving the necessary trade-off. In the scheme, an improved Minutia Cylinder-Code (MCC) based fingerprint authentication algorithm is proposed for the IMD by reducing the length of each feature vector and the number of query feature vectors. Experimental results show that the improved fingerprint authentication algorithm significantly reduces both the size of messages in transmission and computational overheads in the device, and thus can be utilized to secure the IMD. Compared to existing electrocardiogram signal-based security schemes, the F2H scheme does not require the IMD to capture or process biometric traits in every access attempt since a fingerprint template is generated and stored in the IMD beforehand. As a result, the scarce resources in the IMD are conserved, making the scheme sustainable as well as energy efficient.

Wearable and Implantable Medical Devices (WIMDs) perform critical health monitoring and therapeutic functions. However, current WIMD products lack security safeguards to protect patients from fatal cyber-attacks. In the recent past, electrocardiogram (ECG) signals based security techniques have been widely explored to secure such devices by using two cryptographic primitives, the fuzzy commitment and the fuzzy vault, respectively. Nonetheless, differences, as well as similarities between these two primitives, have not been well investigated, making it difficult to decide which one would be appropriate for a particular setting. In this paper, we perform a critical analysis on both primitives and discuss their merits and drawbacks in the context of ECG-based key distribution. We analyze the critical challenges within each primitive-based key distribution technique, such as binary sequence generation and polynomial computations. Experimental results show that the technique based on the fuzzy commitment has a better false acceptance rate due to the randomness of ECG binary sequences. On the other hand, the fuzzy vault based scheme can achieve an acceptable false reject rate (5%) with less cost to the WIMDs. Future research is suggested to enhance the precision of ECG signal processing, to improve the efficacy of binary sequence generation process as well as to suggest ways to reduce polynomial computations.