Finger Vein Template Protection with Directional Bloom Filter

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD

A review of abnormal behavior detection in activities of daily living

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

Study On The Template Protection Technique In Biometric Authentication System

Template protection is a relatively new research direction spurred by the need to resolve security and privacy risks associated with the storage of biometric user templates. Specifically, it can be classified into two main approaches, i.e. Cancelable Biometrics (CB) and Biometric Encryption (BE)

Security And Privacy Risk Assessment on Mobile Payment Services

Internet technology enhances smartphone use and mobile payment technology. • Mobile payment apps contain credit and debit card information and allow us to make transactions on our phones. • Mobile payments carry security risks. Cybercriminals exploit technology flaws to steal money

Fuzzy key extraction from fingerprint biometrics based on dynamic quantization mechanism

Traditional biometrics recognition system is vulnerable to privacy invasion when the stored biometric template is compromised. This in turn will suffer from permanently loss as biometric template is not replaceable In this paper, we propose a key extraction scheme which locks a secure transformed fingerprint bitstring via a novel dynamic quantization mechanism. During authentication stage, the key is extracted from the secure mixture when a genuine fingerprint is presented. A number of keys can be assigned to different applications and could be revoked if the key was compromised. The proposed method retrieve key reliably from a genuine fingerprint up to 99.5% success rate. We perform several security and experimental analyses and the results suggest that the scheme is feasible in practice

Inertial sensor fusion for gait recognition with symmetric positive definite Gaussian kernels analysis

Wearable sensor-based gait recognition has received much interest because it is unobtrusive and is user friendly. Many research has been carried out in this area but conventional gait recognition methods are not free from drawbacks. In this paper, accelerometer and gyroscope signals representing gait movements are encoded using covariance matrices. The covariance matrices provide a compact and descriptive representation for the accelerometer and gyroscope signals. Non-singular covariance matrices are inherently Symmetric Positive Define (SPD) matrices. Interpreting such SPD matrices as points on the Riemannian manifold leads to increased performance. However, direct geodesic distance calculation for the matrix manifold may yield a suboptimal result. The proposed method solves this issue by embedding the manifold valued points to a higher dimensional Reproducing Kernel Hilbert Space (RKHS) via Positive Definite Gaussian Kernel functions. Extensive experiments have been conducted on three challenging benchmark datasets and a self-collected dataset. Experiment results testify the performance of the proposed RKHS embedding approach

Secure biometric template protection via randomized dynamic quantization transformation

Fuzzy Commitment Scheme is one of the biometric encryption approaches for biometric template protection. The idea is to bind an identifier with a biometric template in binary format called difference vector during enrollment. Ideally, a difference vector is infeasible to recover either the biometric template or the identifier without any knowledge of the user's biometric data. Yet, this is only valid if the biometric template is uniformly random, but this is not the case in reality. In this paper, we propose a method known as randomized dynamic quantization transformation (RDQT) to binarize biometric data but still highly distinctive among the users and highly random. We demonstrate the implementation in the context of fingerprint biometrics. The experiment results and the security analysis in DB1 (FVC 2002) dataset suggest that the technique is feasible in practical usage

The MMUISD Gait Database and Performance Evaluation Compared to Public Inertial Sensor Gait Databases

Gait recognition is an emerging biometric method that allows an automatic verification of a person by the way he or she walks. This paper presents a new dataset for gait recognition using mobile sensors called MMUISD Gait Database that resembles the real world as closely as possible. The existing public gait databases are acquired in controlled settings. In this study, an Android application is developed to record the human gait signals through inertial measurement unit sensor such as accelerometer and gyroscope with 50 Hz fixed sampling rate. A preliminary evaluation with 80 samples of participant’s data is carried out to assess the gait recognition performance using the new dataset. Time and frequency domain are used to extract gait features from the raw sensors data. The accuracy is assessed using eight classifiers with 10-fold cross validation. The results show that phone positions and orientation affect the gait recognition performance. The MMUISD dataset that introduces such variability provides a good opportunity for researchers to further investigate these challenges