Quality evaluation of mycelial Antrodia camphorata using high-performance liquid chromatography (HPLC) coupled with diode array detector and mass spectrometry (DAD-MS)

<p>Abstract</p> <p>Background</p> <p><it>Antrodia camphorata </it>(AC) is an important fungus native to Taiwanese forested regions. Scientific studies have demonstrated that extracts of AC possess a variety of pharmacological functions. This study aims to identify the full profile fingerprint of nucleosides and nucleobases in mycelial AC and to assess the quality of two commercial mycelial AC products.</p> <p>Methods</p> <p>High-performance liquid chromatography coupled with diode array detector and mass spectrometry was employed to identify the major components in mycelial AC. The chemical separation was carried out using a gradient program on a reverse phase Alltima C₁₈AQ analytical column (250 × 4.6 mm, 5 μm) with the mobile phase consisting of deionized water and methanol.</p> <p>Results</p> <p>Ten nucleosides and nucleobases, two maleimide derivatives, and a sterol were identified as the major constituents in mycelial AC. These groups of chemical compounds constitute the first chromatographic fingerprint as an index for quality assessment of this medicinal fungus.</p> <p>Conclusions</p> <p>This study provides the first chromatographic fingerprint to assess the quality of mycelial AC.</p

Systematic methods for the computation of the directional fields and singular points of fingerprints

The first subject of the paper is the estimation of a high resolution directional field of fingerprints. Traditional methods are discussed and a method, based on principal component analysis, is proposed. The method not only computes the direction in any pixel location, but its coherence as well. It is proven that this method provides exactly the same results as the "averaged square-gradient method" that is known from literature. Undoubtedly, the existence of a completely different equivalent solution increases the insight into the problem's nature. The second subject of the paper is singular point detection. A very efficient algorithm is proposed that extracts singular points from the high-resolution directional field. The algorithm is based on the Poincare index and provides a consistent binary decision that is not based on postprocessing steps like applying a threshold on a continuous resemblance measure for singular points. Furthermore, a method is presented to estimate the orientation of the extracted singular points. The accuracy of the methods is illustrated by experiments on a live-scanned fingerprint databas

CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%

A quality integrated spectral minutiae fingerprint recognition system

Many fingerprint recognition systems are based on minutiae matching. However, the recognition accuracy of minutiae-based matching algorithms is highly dependent on the fingerprint minutiae quality. Therefore, in this paper, we introduce a quality integrated spectral minutiae algorithm, in which the minutiae quality information is incorporated to enhance the performance of the spectral minutiae fingerprint recognition system. In our algorithm, two types of quality data are used. The first is the minutiae reliability, expressing the probability that a given point is indeed a minutia; the second is the minutiae location accuracy, quantifying the error on the minutiae location. We integrate these two types of quality information into the spectral minutiae representation algorithm and achieve a decrease of 1% in equal error rate in the experiment

Pitfall of the Detection Rate Optimized Bit Allocation within template protection and a remedy

One of the requirements of a biometric template protection system is that the protected template ideally should not leak any information about the biometric sample or its derivatives. In the literature, several proposed template protection techniques are based on binary vectors. Hence, they require the extraction of a binary representation from the real- valued biometric sample. In this work we focus on the Detection Rate Optimized Bit Allocation (DROBA) quantization scheme that extracts multiple bits per feature component while maximizing the overall detection rate. The allocation strategy has to be stored as auxiliary data for reuse in the verification phase and is considered as public. This implies that the auxiliary data should not leak any information about the extracted binary representation. Experiments in our work show that the original DROBA algorithm, as known in the literature, creates auxiliary data that leaks a significant amount of information. We show how an adversary is able to exploit this information and significantly increase its success rate on obtaining a false accept. Fortunately, the information leakage can be mitigated by restricting the allocation freedom of the DROBA algorithm. We propose a method based on population statistics and empirically illustrate its effectiveness. All the experiments are based on the MCYT fingerprint database using two different texture based feature extraction algorithms