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

Multi-Instance Finger Vein Template Protection

Finger vein biometrics is used to authenticate or verify the identity of an individual. It is an alternative to knowledge and token based system, since it cannot be misplaced, shared, or stolen

Transforming Finger Vein Template in Multi-instance Scenario

Unlike single biometric systems, multi-instance biometric systems can reduce the risk of user privacy leakage. Meanwhile, maintaining biometric security also requires template protection. However, there is still limited study focusing on template protection in multi-instance biometrics. To address such issues, this paper proposes a method for multiinstance finger vein template protection. In this work, the vein features of different finger instances are extracted at feature fusion and image fusion levels using Gabor filter method, respectively. For feature transformation, the Gabor feature blocks are transformed with Bloom filters to extract the finger vein template for secure storage and verification. When analyzing the performance, the experiments are mainly conducted with the constant key scenario to be compared with performance of the baseline Gabor feature extraction method and the genuine key scenario. It is shown that this proposed method effectively improves system performance, with equal error rate (EER) as low as 5.00% in constant key scenario, while maintaining zero error rate in genuine key scenario. Besides, the proposed method is also examined for the properties of unlinkability and irreversibility as required for a template protection method

Finger Vein Presentation Attack Detection Based on Texture Analysis

Biometrics is an effective way to identify and authenticate users based on their personal traits. Among all kinds of hand-based biometrics, finger vein appears to be emerging biometrics that has received a great attention due to its rich information available and ease for implementation. With finger vein system becoming more and more popular, there have been various attempts to comprise the system. Recent studies reveal the vulnerabilities of finger vein system to presentation attack where the sensory device accepts a fake printed finger vein image and gives access as if it were a genuine attempt. In this study, a presentation attack detection method based on hybrid feature spaces of finger vein texture analysis is proposed. Histogram of oriented gradient operator is applied on different channels of grayscale and color feature spaces to obtain texture information of the histogram descriptors. The proposed method includes two implementations of feature space analysis, namely CHOG1 and CHOG 2 . A well-established publicly available dataset is used to analysis and evaluate the proposed implementations. Experimental results suggest that the combination channels of grayscale and color luminance is able to generate better performance through Support Vector Machine classifier with ACER as low as 0.60% and 0.74% for CHOG 1 and CHOG 2 , respectively. The experiments show that the implementation of CHOG 1 performs slightly better than single channel max gradients of CHOG 2

Multi-Scale Texture Analysis For Finger Vein Anti-Spoofing

In the recent years, finger vein biometrics has been gaining traction in commercial uses. Despite its wide deployment for user authentication, there is still a risk associated with insecure biometric capture process known as presentation attacks where the attacker uses fake finger vein pattern to spoof the finger vein sensor. This raises the need for an efficient method to detect spoofed finger vein images to ensure the security of the system. In this paper, a multi-scale histogram of oriented gradients representation is proposed for presentation attack detection (PAD) with minimal pre-processing step involved. The results are evaluated with a benchmark dataset and compared with the other PAD methods with promising results

Multi-instance finger vein recognition using minutiae matching

Among the various multi-modal biometric approaches, multi-instance biometric appears to be understudied despite it inherits the merits of multimodal biometrics system. Multi-instance biometrics is useful when the signal quality is too low for robust verification. As compared to other multi-modal approach, multi-instance fusion reduces the need of multiple acquisitions using different sensors and thus lessen both transaction time and sensor cost. In this work, we propose a reliable two-stage multi-instance finger vein recognition system based on minutiae matching method by integrating a unified minutia alignment and pruning approach using Genetic algorithm and the k-modified Hausdorff distance (k-MHD) measurement. The proposed method is evaluated by using the SDUMLA-HMT Finger Vein database. Experiments show the proposed method is able to attain promising recognition rate compared to its single biometrics counterpart. The best result is achieved by applying the k-nearest neighbor measurement alongside, where the recognition rate can be up to 99.7% when MHD is used for matching

Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition

The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, ranging from the index, middle, and ring finger are combined to form a multi-instance finger vein representation. This fusion approach is preferred since it can be performed without requiring additional sensors or feature extractors. To combine different instances of finger vein effectively, score level fusion is adopted to allow greater compatibility among the wide range of matches. Towards this end, two methods are proposed: Bayesian optimized support vector machine (SVM) score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF). The fusion results are incrementally improved by optimizing the hyperparameters of the HOG feature, SVM matcher, and the weighted sum of score level fusion using the Bayesian optimization approach. This is considered a kind of knowledge-based approach that takes into account the previous optimization attempts or trials to determine the next optimization trial, making it an efficient optimizer. By using stratified cross-validation in the training process, the proposed method is able to achieve the lowest EER of 0.48% and 0.22% for the SDUMLA-HMT dataset and UTFVP dataset, respectively