Fingerprint matching using the onion peeling approach and turning function

Fingerprint, as one of the most popular and robust biometric traits, can be used in automatic identification and verification systems to identify individuals. Fingerprint matching is a vital and challenging issue in fingerprint recognition systems. Most fingerprint matching algorithms are minutiae-based. The minutiae points are the ways that the fingerprint ridges can be discontinuous. Ridge ending and ridge bifurcation are two frequently used minutiae in most fingerprint matching algorithms. This article presents a new minutiae-based fingerprint matching using the onion peeling approach. In the proposed method, fingerprints are aligned to find the matched minutiae points. Then, the nested convex polygons of matched minutiae points are constructed and the comparison between peer-to-peer polygons is performed by the turning function distance. Simplicity, accuracy, and low time complexity of the onion peeling approach are three important factors that make it a standard method for fingerprint matching purposes. The performance of the proposed algorithm is evaluated on the database FVC2002. Since the fingerprints that the difference between the number of their layers is more than 2 and the a minutiae matching score lower than 0.15 are ignored, better results are obtained.KeywordsFingerprint Matching, Minutiae, Convex Layers, Turning Function, Computational Geometry.     

A Memory-Efficient Deterministic Finite Automaton-Based Bit-Split String Matching Scheme Using Pattern Uniqueness in Deep Packet Inspection

This paper proposes a memory-efficient bit-split string matching scheme for deep packet inspection (DPI). When the number of target patterns becomes large, the memory requirements of the string matching engine become a critical issue. The proposed string matching scheme reduces the memory requirements using the uniqueness of the target patterns in the deterministic finite automaton (DFA)-based bit-split string matching. The pattern grouping extracts a set of unique patterns from the target patterns. In the set of unique patterns, a pattern is not the suffix of any other patterns. Therefore, in the DFA constructed with the set of unique patterns, when only one pattern can be matched in an output state. In the bit-split string matching, multiple finite-state machine (FSM) tiles with several input bit groups are adopted in order to reduce the number of stored state transitions. However, the memory requirements for storing the matching vectors can be large because each bit in the matching vector is used to identify whether its own pattern is matched or not. In our research, the proposed pattern grouping is applied to the multiple FSM tiles in the bit-split string matching. For the set of unique patterns, the memory-based bit-split string matching engine stores only the pattern match index for each state to indicate the match with its own unique pattern. Therefore, the memory requirements are significantly decreased by not storing the matching vectors in the string matchers for the set of unique patterns. The experimental results show that the proposed string matching scheme can reduce the storage cost significantly compared to the previous bit-split string matching methods.     

Fingerprint image enhancement using CNN filtering techniques

Due to noisy acquisition devices and variation in impression conditions, the ridgelines of fingerprint images are mostly corrupted by various kinds of noise causing cracks, scratches and bridges in the ridges as well as blurs. These cause matching errors in fingerprint recognition. For an effective recognition the correct ridge pattern is essential which requires the enhancement of fingerprint images. Segment by segment analysis of the fingerprint pattern yields various ridge direction and frequencies. By selecting a directional filter with correct filter parameters to match ridge features at each point, we can effectively enhance fingerprint ridges. This paper proposes a fingerprint image enhancement based on CNN Gabor-Type filters.     

Error tolerant searching of uninterpreted tandem mass spectrometry data

An error tolerant mode for database matching of uninterpreted tandem mass spectrometry data is described. Selected database entries are searched without enzyme specificity, using a comprehensive list of chemical and post-translational modifications, together with a residue substitution matrix. The modifications are tested serially, to avoid the catastrophic loss of discrimination that would occur if all the permutations of large numbers of modifications in combination were possible. The new mode has been coded as an extension to the Mascot search engine, and tested against a number of Liquid chromatography-tandem mass spectrometry datasets. The results show a number of additional peptide matches, but require careful interpretation. The most significant limitation of this approach is that it can only reveal new matches to proteins that already have at least one significant peptide match.     

Recollection ability of three-dimensional correlation matrix associative memory

This paper presents the approximate expression of the recollection probability of a three-dimensional correlation matrix autoassociative memory, in which each memorized pattern consists of binary (+1 or-1) elements, and discusses the recollection ability in comparison with that of the conventional two-dimensional correlation matrix associative memory. An associative memory using the correlation properties between memorized patterns desires the condition that the memorized patterns are mutually orthogonal or approximately orthogonal. The three-dimensional correlation matrix associative memory that inscribes the third order correlation of a memorized pattern heightens the recollection ability even though the above condition is not satisfied.     

A protein database constructed from low-coverage genomic sequence of Bacillus megaterium and its use for accelerated proteomic analysis

Peptide mass fingerprint (PMF) matching is a high-throughput method used for protein spot identification in connection with two-dimensional gel electrophoresis (2DE). However, the success of PMF matching largely depends on whether the proteins to be identified exist in the database searched. Consequently, it is often necessary to apply other more sophisticated but also time-consuming technologies to generate sequence-tags for definitive protein identification. On the other hand, modern sequencing technologies are generating a large quantity of DNA sequences, first in unfinished form or with low genome coverage due to the time-consuming and thus limiting steps of finishing and annotation. We recently started to sequence the genome of Bacillus megaterium DSM 319, a bacterium of industrial interest. In this study, we demonstrate that a protein database generated from merely three-fold coverage, unfinished genomic sequences of this bacterium allows a fast and reliable protein spot identification solely based on PMF from high-throughput MALDI-TOF MS analysis. We further show that the strain-specific protein database from low coverage genomic sequence greatly outperforms the commonly used cross-species databases constructed from 13 completely sequenced Bacillus strains for protein spot identification via PMF.     

Reducing storage requirements for biological sequence comparison

MOTIVATION: Comparison of nucleic acid and protein sequences is a fundamental tool of modern bioinformatics. A dominant method of such string matching is the 'seed-and-extend' approach, in which occurrences of short subsequences called 'seeds' are used to search for potentially longer matches in a large database of sequences. Each such potential match is then checked to see if it extends beyond the seed. To be effective, the seed-and-extend approach needs to catalogue seeds from virtually every substring in the database of search strings. Projects such as mammalian genome assemblies and large-scale protein matching, however, have such large sequence databases that the resulting list of seeds cannot be stored in RAM on a single computer. This significantly slows the matching process. RESULTS: We present a simple and elegant method in which only a small fraction of seeds, called 'minimizers', needs to be stored. Using minimizers can speed up string-matching computations by a large factor while missing only a small fraction of the matches found using all seeds.     

Ensemble dynamics of hippocampal regions CA3 and CA1

Computational models based on hippocampal connectivity have proposed that CA3 is uniquely positioned as an autoassociative memory network, capable of performing the competing functions of pattern completion and pattern separation. Recently, three independent studies, two using parallel neurophysiological recording methods and one using immediate-early gene imaging, have examined the responses of CA3 and CA1 ensembles to alterations of environmental context in rats. The results provide converging evidence that CA3 is capable of performing nonlinear transformations of sensory input patterns, whereas CA1 may represent changes in input in a more linear fashion.     

Palm-Vein Classification Based on Principal Orientation Features

Personal recognition using palm–vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm–vein pattern recognition, the corresponding growth of the database has resulted in a long response time. To shorten the response time of identification, this paper proposes a simple and useful classification for palm–vein identification based on principal direction features. In the registration process, the Gaussian-Radon transform is adopted to extract the orientation matrix and then compute the principal direction of a palm–vein image based on the orientation matrix. The database can be classified into six bins based on the value of the principal direction. In the identification process, the principal direction of the test sample is first extracted to ascertain the corresponding bin. One-by-one matching with the training samples is then performed in the bin. To improve recognition efficiency while maintaining better recognition accuracy, two neighborhood bins of the corresponding bin are continuously searched to identify the input palm–vein image. Evaluation experiments are conducted on three different databases, namely, PolyU, CASIA, and the database of this study. Experimental results show that the searching range of one test sample in PolyU, CASIA and our database by the proposed method for palm–vein identification can be reduced to 14.29%, 14.50%, and 14.28%, with retrieval accuracy of 96.67%, 96.00%, and 97.71%, respectively. With 10,000 training samples in the database, the execution time of the identification process by the traditional method is 18.56 s, while that by the proposed approach is 3.16 s. The experimental results confirm that the proposed approach is more efficient than the traditional method, especially for a large database.     

Quantitative estimate of the information relayed by the Schaffer collaterals

Within the theory that describes the hippocampus as a device for the on-line storage of complex memories, the crucial autoassociative operations are ascribed mainly to the recurrent CA3 network. The CA3-to-CA1 connections may still be important, both in completing information retrieval and in re-expanding, with minimal information loss, the highly compressed representation retrieved in CA3. To quantify these effects, I have defined a suitably realistic formal model of the relevant circuitry, and evaluated its performance in the sense of information theory. Analytical estimates, calculated with mean-field, replica and saddle-point techniques, of the amount of information present in the model CA1 output, reveal how such performance depends on different parameters characterising these connections. In particular, nearly all the stored information can be preserved if the model Schaffer collaterals are endowed with an optimal degree of Hebbian plasticity, matching that of the CA3 recurrent collaterals.     

Manta Matcher: automated photographic identification of manta rays using keypoint features

For species which bear unique markings, such as natural spot patterning, field work has become increasingly more reliant on visual identification to recognize and catalog particular specimens or to monitor individuals within populations. While many species of interest exhibit characteristic markings that in principle allow individuals to be identified from photographs, scientists are often faced with the task of matching observations against databases of hundreds or thousands of images. We present a novel technique for automated identification of manta rays (Manta alfredi and Manta birostris) by means of a pattern‐matching algorithm applied to images of their ventral surface area. Automated visual identification has recently been developed for several species. However, such methods are typically limited to animals that can be photographed above water, or whose markings exhibit high contrast and appear in regular constellations. While manta rays bear natural patterning across their ventral surface, these patterns vary greatly in their size, shape, contrast, and spatial distribution. Our method is the first to have proven successful at achieving high matching accuracies on a large corpus of manta ray images taken under challenging underwater conditions. Our method is based on automated extraction and matching of keypoint features using the Scale‐Invariant Feature Transform (SIFT) algorithm. In order to cope with the considerable variation in quality of underwater photographs, we also incorporate preprocessing and image enhancement steps. Furthermore, we use a novel pattern‐matching approach that results in better accuracy than the standard SIFT approach and other alternative methods. We present quantitative evaluation results on a data set of 720 images of manta rays taken under widely different conditions. We describe a novel automated pattern representation and matching method that can be used to identify individual manta rays from photographs. The method has been incorporated into a website (mantamatcher.org) which will serve as a global resource for ecological and conservation research. It will allow researchers to manage and track sightings data to establish important life‐history parameters as well as determine other ecological data such as abundance, range, movement patterns, and structure of manta ray populations across the world.     

Adaptive pattern classification and universal recoding: II. Feedback,expectation, olfaction,illusions

Part I of this paper describes a model for the parallel development and adult coding of neural feature detectors. It shows how any set of arbitrary spatial patterns can be recoded, or transformed, into any other spatial patterns (universal recoding), if there are sufficiently many cells in the network's cortex. This code is, however, unstable through time if arbitrarily many patterns can perturb a fixed number of cortical cells. This paper shows how to stabilize the code in the general case using feedback between cellular sites. A biochemically defined critical period is not necessary to stabilize the code, nor is it sufficient to ensure useful coding properties.We ask how short term memory can be reset in response to temporal sequences of spatial patterns. This leads to a context-dependent code in which no feature detector need uniquely characterize an input pattern; yet unique classification by the pattern of activity across feature detectors is possible. This property uses learned expectation mechanisms whereby unexpected patterns are temporarily suppressed and/or activate nonspecific arousal. The simplest case describes reciprocal interactions via trainable synaptic pathways (long term memory traces) between two recurrent on-center off-surround networks undergoing mass action (shunting) interactions. This unit can establish an adaptive resonance, or reverberation, between two regions if their coded patterns match, and can suppress the reverberation if their patterns do not match. This concept yields a model of olfactory coding within the olfactory bulb and prepyriform cortex. The resonance idea also includes the establishment of reverberation between conditioned reinforcers and generators of contingent negative variation if presently avialable sensory cues are compatible with the network's drive requirements at that time; and a search and lock mechanism whereby the disparity between two patterns can be minimized and the minimal disparity images locked into position. Stabilizing the code uses attentional mechanisms, in particular nonspecific arousal as a tuning and search device. We suggest that arousal is gated by a chemical transmitter system—for example, norepinephrine—whose relative states of accumulation at antagonistic pairs of on-cells and off-cells through time can shift the spatial pattern of STM activity across a field of feature detectors. For example, a sudden arousal increment in response to an un-expected pattern can reverse, or rebound, these relative activities, thereby suppressing incorrectly classified populations. The rebound mechanism has formal properties analogous to negative afterimages and spatial frequency adaptation.Supported in part by the Advanced Research Projects Agency under ONR Contract No. N00014-76-C-0185     

Potential for false positive identifications from large databases through tandem mass spectrometry

The biomedical research community at large is increasingly employing shotgun proteomics for large-scale identification of proteins from enzymatic digests. Typically, the approach used to identify proteins and peptides from tandem mass spectral data is based on the matching of experimentally generated tandem mass spectra to the theoretical best match from a protein database. Here, we present the potential difficulties of using such an approach without statistical consideration of the false positive rate, especially when large databases, as are encountered in eukaryotes are considered. This is illustrated by searching a dataset generated from a multidimensional separation of a eukaryotic tryptic digest against an in silico generated random protein database, which generated a significant number of positive matches, even when previously suggested score filtering criteria are used.     

Performance evaluation of a remote memory system with commodity hardware for large-memory data processing

The explosion of data and transactions demands a creative approach for data processing in a variety of applications. Research on remote memory systems (RMSs), so as to exploit the superior characteristics of dynamic random access memory (DRAM), has been performed for many decades, and today’s information explosion galvanizes researchers into shedding new light on the technology. Prior studies have mainly focused on architectural suggestions for such systems, highlighting different design rationale. These studies have shown that choosing the appropriate applications to run on an RMS is important in fully utilizing the advantages of remote memory. This article provides an extensive performance evaluation for various types of data processing applications so as to address the efficacy of an RMS by means of a prototype RMS with reliability functionality. The prototype RMS used is a practical kernel-level RMS that renders large memory data processing feasible. The abstract concept of remote memory was materialized by borrowing unused local memory in commodity PCs via a high speed network capable of Remote Direct Memory Access (RDMA) operations. The prototype RMS uses remote memory without any part of its computation power coming from remote computers. Our experimental results suggest that an RMS can be practical in supporting the rigorous demands of commercial in memory database systems that have high data access locality. Our evaluation also convinces us of the possibility that a reliable RMS can satisfy both the high degree of reliability and efficiency for large memory data processing applications whose data access pattern has high locality.     

Memories in context

Context-dependent associative memories are models that allow the retrieval of different vectorial responses given a same vectorial stimulus, depending on the context presented to the memory. The contextualization is obtained by doing the Kronecker product between two vectorial entries to the associative memory: the key stimulus and the context. These memories are able to display a wide variety of behaviors that range from all the basic operations of the logical calculus (including fuzzy logics) to the selective extraction of features from complex vectorial patterns. In the present contribution, we show that a context-dependent memory matrix stores a large amount of possible virtual associative memories, that awaken in the presence of a context. We show how the vectorial context allows a memory matrix to be representable in terms of its singular-value decomposition. We describe a neural interpretation of the model in which the Kronecker product is performed on the same neurons that sustain the memory. We explored, with numerical experiments, the reliability of chains of contextualized associations. In some cases, random disconnection produces the emergence of oscillatory behaviors of the system. Our results show that associative chains retain their performances for relatively large dimensions. Finally, we analyze the properties of some modules of context-dependent autoassociative memories inserted in recursive nets: the perceptual autoorganization in the presence of ambiguous inputs (e.g. the disambiguation of the Necker’s cube figure), the construction of intersection filters, and the feature extraction capabilities.     

Automatically annotating documents with normalized gene lists

BACKGROUND: Document gene normalization is the problem of creating a list of unique identifiers for genes that are mentioned within a document. Automating this process has many potential applications in both information extraction and database curation systems. Here we present two separate solutions to this problem. The first is primarily based on standard pattern matching and information extraction techniques. The second and more novel solution uses a statistical classifier to recognize valid gene matches from a list of known gene synonyms. RESULTS: We compare the results of the two systems, analyze their merits and argue that the classification based system is preferable for many reasons including performance, simplicity and robustness. Our best systems attain a balanced precision and recall in the range of 74%-92%, depending on the organism.     

A new image processing technique for determination of cell-generated deformations on substrata

This paper introduces a new image processing technique that determines the displacement field of a given substrate from “null-force” and “force-loaded” images. In this method, fluorescent elements used to track motion, which will be referred to as beads, can be seen in these images by locating the gray value that is normally distributed around their central point. Next comes a two-step process of matching the beads with displacements. The first step matches the beads with a small displacement using the correlation function of the characteristic pixels. Based on results from this initial step, another correlation function determines a pair of beads with a relatively large displacement. The entire matching process is done in this way, gradually working from the small displacement to the large one. Finally, using the cubic spline weight function, the whole displacement field is interpolated and filtered out of those displacements, which were initially found with the matched beads. Applying this new method on the cell migration yields satisfying results. Based on the particle tracking, the displacement field obtained by this new image processing technique has clear physical meaning. More importantly, this new method completes the matching of the displacement using the features of the displacement field, thus avoiding the direct matching with the image gray values for the relatively large strain of the substrate around the cell. Accordingly, it greatly decreases mismatching, making data checking unnecessary.