The non-enzymatic reaction between reducing sugars and proteins, known as glycation, has received increased attention from nutritional and medical research. In addition, there is a large interest in obtaining glycoconjugates of pure well-characterized oligosaccharides for biological research. In this study, glycation of bovine serum albumin (BSA) by d-glucose, d-galactose and d-lactose under dry-heat at 60 degrees C for 30, 60, 120, 180 or 240 min was assessed and the glycated products studied in order to establish their biological recognition by lectins. BSA glycation was monitored using gel electrophoresis, determination of available amino groups and lectin binding assays. The BSA molecular mass increase and glycation sites were investigated by mass spectrometry and through digestion with trypsin and chymotrypsin. Depending on time and type of sugar, differences in BSA conjugation were achieved. Modified BSA revealed reduction of amino groups' availability and slower migration through SDS/PAGE. d-galactose was more reactive than d-glucose or d-lactose, leading to the coupling of 10, 3 and 1 sugar residues, respectively, after 120 minutes of reaction. BSA lysines (K) were the preferred modified amino acids; both K256 and K420 appeared the most available for conjugation. Only BSA-lactose showed biological recognition by specific lectins.

https://ojs.ptbioch.edu.pl/index.php/abp/article/download/3054/2115

Characterization of bovine serum albumin glycated with glucose, galactose and lactose.

Fingerprints are the oldest and most widely used form of biometric identification. Everyone is known to have unique, immutable fingerprints. As most Automatic Fingerprint Recognition Systems are based on local ridge features known as minutiae, marking minutiae accurately and rejecting false ones is very important. However, fingerprint images get degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction. A critical step in automatic fingerprint matching is to reliably extract minutiae from the input fingerprint images. This paper presents a review of a large number of techniques present in the literature for extracting fingerprint minutiae. The techniques are broadly classified as those working on binarized images and those that work on gray scale images directly.

/pdf/minutiae-extraction-from-fingerprint-images-a-review-a439np8pxb.pdf

Minutiae Extraction from Fingerprint Images - a Review

Sweat pores on fingerprints have proven to be useful features for personal identification. Several methods have been proposed for pore matching. The state-of-the-art method first matches minutiae on the fingerprints and then matches the pores based on the minutia matching results. A problem of such minutia-based pore matching method is that the pore matching is dependent on the minutia matching. Such dependency limits the pore matching performance and impairs the effectiveness of the fusion of minutia and pore match scores. In this paper, we propose a novel direct approach for matching fingerprint pores. It first determines the correspondences between pores based on their local features. It then uses the RANSAC (RANdom SAmple Consensus) algorithm to refine the pore correspondences obtained in the first step. A similarity score is finally calculated based on the pore matching results. The proposed pore matching method successfully avoids the dependency of pore matching on minutia matching results. Experiments have shown that the fingerprint recognition accuracy can be greatly improved by using the method proposed in this paper.

/pdf/direct-pore-matching-for-fingerprint-recognition-14wkif72ih.pdf

Direct Pore Matching for Fingerprint Recognition

To enhance low-quality fingerprint images, we present a novel method that first estimates the local orientation of the fingerprint ridge and valley flow and next performs oriented diffusion filtering, followed by a locally adaptive contrast enhancement step. By applying the authors' new approach to low-quality images of the FVC2004 fingerprint databases, the authors are able to show its competitiveness with other state-of-the-art enhancement methods for fingerprints like curved Gabor filtering. A major advantage of oriented diffusion filtering over those is its computational efficiency. Combining oriented diffusion filtering with curved Gabor filters led to additional improvements and, to the best of the authors' knowledge, the lowest equal error rates achieved so far using MINDTCT and BOZORTH3 on the FVC2004 databases. The recognition performance and the computational efficiency of the method suggest to include oriented diffusion filtering as a standard image enhancement add-on module for real-time fingerprint recognition systems. In order to facilitate the reproduction of these results, an implementation of the oriented diffusion filtering for Matlab and GNU Octave is made available for download.

Oriented diffusion filtering for enhancing low-quality fingerprint images

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.

/pdf/filter-design-and-performance-evaluation-for-fingerprint-sie3howeib.pdf

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Fingerprint image quality is an important factor in the performance of Automatic Fingerprint Identification Systems(AFIS). It is used to evaluate the system performance, assess enrollment acceptability, and evaluate fingerprint sensors. This paper presents a novel methodology for fingerprint image quality measurement. We propose limited ring-wedge spectral measure to estimate the global fingerprint image features, and inhomogeneity with directional contrast to estimate local fingerprint image features. Experimental results demonstrate the effectiveness of our proposal.

/pdf/image-quality-measures-for-fingerprint-image-enhancement-57vus0mcwy.pdf

Image quality measures for fingerprint image enhancement

Fingerprint identification is commonly used in authentication and forensic applications and is by far the most common biometric modality in use today. We have developed several novel image processing routines to address the problems with current Automatic Fingerprint Identification Systems (AFIS) which fail to perform when the fingerprint images are of poor quality. 
Current image enhancement methods are not sensitive to the type and quality of fingerprints. We have developed an adaptive filter, which automatically selects the preprocessing parameters based on the image quality. Our method uses a band-limited ring-wedge spectrum energy of the Fourier transform to characterize the global flow pattern of the ridges. It combines features from both the frequency domain and the spatial domain to provide a quantitative estimate of the quality which is then used to selectively enhance fingerprint images. Fingerprint ridge flow patterns are categorized into two types: pseudo-parallel ridges and high-curvature ridges surrounding singular points for this purpose. The enhancement filter uses a coherence map to locate the high-curvature regions (delta and core) and Gaussian kernels whose sizes adapt to the local ridge topology. 
Segmentation of the regions of interest is another important preprocessing task. Blockwise segmentation methods described in the literature cannot remove spurious minutiae (features) present at the boundary of blocks. We have developed a novel algorithm that uses the strength property of the Harris corner point which is usually higher in the in ridge regions. These points are filtered based on thresholding (Otsu's method) of the response of the Gabor filter to identify the outliers. The foreground boundary contour is located by a convex hull of the remaining Harris points and spurious boundary minutiae are discarded. 
Thinning based minutiae detection algorithms are computationally intensive and have the problem of minutiae shifting (by the ridge width). Irregularities and aberrations in the ridge flow also tend to cause spurious minutiae. We have developed two thinning-free minutiae extraction methods based on chain-code contour tracing and run length scanning. Our image processing framework has been tested on the FVC2002 DB1 and DB4 datasets each containing 800 fingerprints and compared with benchmark software developed by the National Institute of Standards and Technology (NIST). We have achieved an equal error rate (EER) improvement of 15% on the DB1 set and 37% on the DB4 set in the comparative tests.

Advanced feature extraction algorithms for automatic fingerprint recognition systems

Traditional classifier combination algorithms use either non-trainable combination functions or functions trained with the goal of better separation of genuine and impostor class matching scores. Both of these approaches are suboptimal if the system is intended to perform identification of the input among few enrolled classes or templates. In this work we propose training combination functions with the goal of minimizing the misclassification rate. The main idea of proposed methods is to use a set of best or strong impostors, and attempt to construct a classifier combination function separating genuine and best impostor matching scores. We have to use iterative methods for such training, since the set of best impostors depends on currently used combination function. We present two iterative methods for constructing combination functions and perform experiments on handwritten word recognizers and biometric matchers.

/pdf/iterative-methods-for-searching-optimal-classifier-2kvrhvqvqi.pdf

Iterative Methods for Searching Optimal Classifier Combination Function

We have investigated different scenarios of combining pattern matchers. The combination problem can be viewed as a construction of a postprocessing classifier operating on the matching scores of the combined matchers. The optimal combination algorithm for verification systems corresponds to the likelihood ratio combination function. It can be implemented by the direct reconstruction of this function with genuine and impostor score density approximations. However, the optimal combination algorithm for identification systems is difficult to express analytically. We will show that this difficulty is caused by the dependencies between matching scores assigned to different classes by the same classifier. The experiments on the large sets of scores from handwritten word recognizers operating on postal images and biometric matchers (NIST biometric score set BSSR1) confirm the existence of such dependencies and that the optimal combination functions for verification and identification systems are different.

On the Difference Between Optimal Combination Functions for Verification and Identification Systems

Matching systems can be used in different operation tasks such as verification task and identification task. Different optimization criteria exist for these tasks - reducing cost of acceptance decisions for verification systems and minimizing misclassification rate for identification systems. In this paper we show that the optimal combination rules satisfying these criteria are also different. The difference is caused by the dependence of matching scores produced by a single matcher and assigned to different classes. We illustrate the theory by experiments with biometric matchers and handwritten word recognizers.

Optimal classifier combination rules for verification and identification systems

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