MKcken M, Newell AC. 19, no. However permanent scarring can occur with deep lesions. A. L. Caterini and D. E. Chang, Recurrent Neural Networks, 2018. Id heard of loops, arches and whorls before, and latent prints, but I had no idea all of that was just the tip of the iceberg. Other approaches are statistical-based, neural network-based, and multiclassifier-based [7]. The core is the approximate centre of the fingerprint impression, where the friction ridges converge. Latent fingerprints hidden prints left by the transfer of oils or other body secretions. In the verification stage, the decision is determined based on the percentages or matching results of similarity. 30, no. I. Jawarneh and N. Alsharman, The classification of arch fingerprint using mathematical model and deep learning features selection, International Journal of Mathematics and Computer Science, vol. A picture paints a thousand words. When a ridge forks or diverges into branch ridges, it is called ridge bifurcation. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. The Writers Police Academy is by far the best conference Ive ever attended. Figure 5 shows the phase portrait of the concentric whorl, Figures 6 and 7 show the phase portrait for the spiral whorl, and Figure 8 shows the phase portrait for the composite with the S core. You guys have all the cool stuff. 9, pp. In another study conducted by Lee et al. Fingerprint that contains a dot with two ridge lines. ending" and "ridge bifurcation" are the most commonly used, since all the other types of minutiae can be seen as . This part of a fingerprint looks like a hook coming off of another ridge. Fingerprints are the pattern of the epidermal ridges on fingers. Several techniques for fingerprint matching have been proposed. The point at, or in front of, and nearest the center of, the divergence of the Type Line. These include trauma, benign and malignant skin lesions, infections and inflammatory skin conditions. 6, pp. The rest of the paper is organized as follows: Section 2 explains the related work. <3. Humans make mistakes. Biometrics science is used to identify people using their physical characteristics. This looks like an oval or an eye. No. K. Anil, Jain, salil prabhakar, student member, and lin Hong. This involves the encoding of the singular points (core and delta) with direction and location. A. Nagar, K. Nandakumar, and A. K. Jain, Securing fingerprint template: fuzzy vault with minutiae descriptors, in Proceedings of the 2008 International Conference for Pattern Recognition, Tampa, USA, December 2008. 3, pp. I knew, instinctively, that fingerprint analysis was complicated, but I had no idea the depth and breadth of the science. A pattern that looks like a blend of the various types of patterns seen. Therefore, leaving a comment is considered a clear affirmative, specific, and unambiguous action as defined by the GDPR. Then, the pattern of the input image is determined. This is where it becomes more difficult. Gastrulation early single ball of cells reorganises into three, Endoderm forms other major organ systems including gastrointestinal tract, respiratory tract, urinary tract. end. There exist four main types of fingerprint reader hardware: Spots are those fingerprint ridges that are significantly shorter than other ridges. gq@U(c,16q{GxYl'k@0BG9rrm: D q\%x/ So the dark areas of the fingerprint are called ridges and white area that exists between them is known as valleys. J.-H. Hong, J.-K. Min, U.-K. Cho, and S.-B. arrow-right-small-blue A ridge bifurcation minutia is a point where a ridge splits from a single path to two paths, while a ridge ending minutia is a point where a ridge terminates. M. Lin, Q. Chen, and S. Yan, Network in Network, 2014. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively. The core is the centre of the fingerprint as that is where the ridges converge. In todays post, lets look at fingerprints. Hook (spur) One line branching off from the others. If a fingerprint has two Deltas, its classified as a whorl. Simulation of the plain fingerprint: (a) phase portrait of the model and (b) image of the plain fingerprint [. 16, no. endstream endobj 806 0 obj <>/Metadata 96 0 R/Pages 803 0 R/StructTreeRoot 136 0 R/Type/Catalog>> endobj 807 0 obj <>/MediaBox[0 0 595.32 841.92]/Parent 803 0 R/Resources<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI]/XObject<>>>/Rotate 0/StructParents 0/Tabs/S/Type/Page>> endobj 808 0 obj <>stream It is formed by a single friction ridge that bifurcates and rejoins after a short course and continues as a single friction ridge. The classification scheme is based on the extraction of the fingerprint feature, which involves encoding the singular points along with their relative positions and directions from a fingerprint image of the binaries. This minutia is an example of a second-level fingerprint detail. Whats really scary is that they rely on the individual examiner to confirm a match. A free database biometric authentication system is presented, with a tamper-resistant smartcard serving as the storage device. The type of fingerprint on the left is a loop. Tented arches are more pointed compared to a plain arch. Your blog is properly stuffed with information about fingerprints. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. of bifurcation point and 0 ridges.pls help. Its a fascinating field. Virtual and In-Person Appearances & Events. 408, pp. 2011. When a ridge connects two ridges, like a bridge. Fingerprint Dive into the research topics of 'Comparison of 2-stenting strategies depending on sequence or technique for bifurcation lesions in the second-generation drug-eluting stent era -analysis from the cobis (coronary bifurcation stenting) iii registry -'. Imbinarize uses a 256-bin image histogram to compute Otsus threshold. The point at which one friction ridge divides into two friction ridges. This is Part 2- Learn how to lift a print, see your finger under the dissecting scope, and learn the differences between lakes, islands, bifurcations, trifurcations, hooks, etc.. W. Zhang, L. L. Tang, Q. Li, A. Liu, and M. L. T. Lee, Orderrestricted inference for clustered ROC data with application to fingerprint matching accuracy, Biometrics, vol. 389404, 1996. 12081216, 2021. They can How To Defend Yourself Against Identity Theft. Who believes that man is a bifurcated nature? But then, their fingerprint would be even more distinguishable because of the scar. heh heh AlexNet-8 architecture classifier results. 1, pp. In the next section, we present the proposed model for fingerprint classification based on neural networks and matching based on bifurcation minutiae extraction. This research paper establishes correspondence between two fingerprints based on ridge ending and bifurcation points and implements fingerprint recognition algorithm using minutiae matching with the help of an image processing with programming tool MATLAB. TechnologyAdvice does not include all companies or all types of products available in the marketplace. Because inking, pressure, failure to roll fingerprints nail-to-nail, and scars can all change the appearance of the characteristics. A finger minutia is a fingerprint ridge ending, or a ridge bifurcation where the ridge separates into two ridges - the characteristics that make each fingerprint unique. Please join me Friday when I interview Larry Brooks. Individualistic details of various levels that are observed within a fingerprint and are used to individually characterize the fingerprint in question and be used for inclusion and exclusion purposes. The match is left to the individual Fingerprint Examiner. The technique takes a binary image of a fingerprint and makes the ridges that appear in print just one pixel wide without changing the overall pattern and leaving gaps in the ridges creating a sort of skeleton of the image. [18]. This minutia is an example of a second-level fingerprint detail. And Canada also has a mandatory second opinion from an independent fingerprint examiner. Any cookies on my website are used to ensure normal website functions (for instance, Youtube videos wont work without their own identifiers). For example, if someone sliced their fingertip with a knife. When a ridge breaks in two and then joins back into one a short time later. If a fingerprint has no Deltas, its classified as an arch. The most commonly used minutiae in current fingerprint recognition technologies are ridge endings and bifurcations, because they can be easily detected by only looking at points that surround them (Bifurcation is the location where a ridge divides into two separate ridges). 744747, 2002. 11, pp. Moreover, AlexNet provides better results in terms of time training. Thank you! She's appeared on the Emmy award-winning true crime series, Storm of Suspicion, and three episodes of A Time to Kill on DiscoveryID (airs in 2023). We have considered three mathematical systems of the whorl patterns as mentioned above in the categorization method: concentric, spiral, and composite with S core [4]. To categorize the dataset into three main fingerprint patterns, mathematical systems for each pattern were used to identify pattern types. Worst airline ever. 18, no. The process of fingerprint identification is to confirm or refuse if a scanned fingerprint belongs to a specific person or not. Collectively, these features are called minutiae. Determination made by the examiner that there is sufficient reliable detail of characteristics in disagreement that one can conclude that two fingerprints (friction ridge impressions) did not originate from the same source. A fingerprint image has a lot of redundant information when it is captured. 197387, 2014. Friction ridges are also found on our palms, feet, and toes. 37, no. 55, no. Dec 2011. 346359, 2008. In GoogleNet, convolutionary layers are substituted in small blocks, as suggested in the Network in Network (NIN) architecture, such as replacing each layer with micro-NN [51]. Because the skin at the ends of fingers, where fingerprint patterns are located is soft, the positions and directions of minutiae are subject to great deal of distortion . It can consist of up to three deltas, may contain a core, and predominantly appears to be a mix of an arch and a loop. Active member of Mystery Writers of America, Sisters in Crime, and International Thriller Writers, Sue Coletta is an award-winning crime writer of serial killer thrillers (Tirgearr Publishing) and true crime (Rowman & Littlefield Group). S Dass and A Jain, Fingerprint classification using orientation field flow curves, in Proceedings of the ICVGIP 2004, Proceedings of the Fourth Indian Conference on Computer Vision, Graphics & Image Processing, Kolkata, India, December 2004. Keep it up . The data have trained with various convolutional layers to find the best architecture of CNN. Identification of fingerprints is the oldest forensic science known to humans. R. Cappelli, A. Lumini, D. Maio, and D. Maltoni, Fingerprint classification by directional image partitioning, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 198212, 2017. To the best of our knowledge, this is the first such attempt to tackle complex fingerprint classification issues using CNN. 662676, 2019. 21, pp. 2D grayscale to 3D color: the main idea to convert from 2D grayscale to 3D color is that the CNNs just accept the image with 3D color so that the following MATLAB function is used for converting: AugmentedTrainingSet=augmentedImageDatastore(imageSize, trainingSet, ColorPreprocessing, gray2rgb); augmentedTestSet=augmentedImage-Datastore (imageSize, testSet, ColorPreprocessing, gray2rgb); The separation of the object and background is known as binarization. 18, no. 7, no. System 5 represents the mathematical model for the plain arch pattern, which is appeared in Figure 9; see [35]. Thinning makes it easier to find minutiae and removes a lot of redundant data that would have resulted in longer process time and sometimes different results [38]. Points are ridge characteristics, and there are as many as 150 points in the average fingerprint. Figure 16 shows the list of parameters setting and their candidate values for different CNN models. On the other hand, the pattern of the spiral has three equilibrium points, the origin is spiral out, and the points (1, 0) and (1, 0) are cusps. This is incredibly cool! A valley is a depression or low point. In the images below youll get a feel of what many of the points look like. In addition, it has three main categories of fingerprints (whorls, arches, and loop). Plain Whorl (found in 24% of the population) have one or more ridges that form a complete spiral, with two Deltas. 206214, 2013. Property of TechnologyAdvice. 103112, 2004. Hopefully youll be doing a post on that one too??? Double Bifurcation. 431454, 2019. Serial Killers by State - FBI Stats - How Many Live Near You? Latent fingerprints can be made visible by dusting with powder or via chemical reaction. Collectively they are called minutiae. System 4 clarifies the mathematical model for the composite whorl with the S core pattern, which is shown in Figure 8; see [4]. Im so glad you found it useful! System 2 represents the mathematical model for the upper right-lower left (UR-LL) spiral whorl pattern, which is shown in Figure 6; see [4]. Fingerprint formation. Love your stuff, always mesemrising, memsemriigning.mesmerising..see, you simply hypnotic , Aww, youre so sweet. The fingerprint minutiae (AKA ridge characteristics) can be linked to one specific person. The authors declare that they have no conflicts of interest. The key goal of GoogleNets architecture was to achieve high precision and reduce computational costs [50]. The overall validation accuracy is 43.75%. On the left side of the image, we can see (from top to bottom) Type Lines, Delta, and more Type Lines. J. Li, Y. Wei-Yun, and H. Wang, Combining singular points and orientation image information for fingerprint classification, Pattern Recognition, vol. 87, pp. 58, 2019. Minutia and patterns are very important in the analysis of fingerprints. In addition, the fingerprint is one of the most accurate and discriminating biometrics that has been investigated and used to identify human beings for hundreds of years [33]. 5, pp. Reduced fingerprint due to dermatitis, Books about skin diseasesBooks about the skin 2, pp. They consist of friction ridge units that can be characterised by patterns (arrangement of ridges) and minutiae (small specific details such as bifurcations and ridge endings). A fingerprint is a distinct pattern of ridges and valleys on the finger surface of an individual. Volar pads less prominent then disappear; friction ridge units develop, Volar skin is layered epidermis on top of shapeless fibrous dermis, By end of second trimester, sweat ducts and pores appear along epidermal ridges. A multichannel approach to fingerprint classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. Section 5 presents results and discussion. The image below shows the points of identification. Plastic fingerprints indentations left in a soft material. In order to learn how to identify fingerprints, we need to know what were looking at and where to find the pattern. endstream endobj 810 0 obj <>stream A Abdolrashidi, Fingernet: pushing the limits of fingerprint recognition using convolutional neural network, CoRR, abs/, 2019. Loss is used to optimize a deep learning algorithm. D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. In biometrics and fingerprint scanning, bifurcation refers to the point in a fingerprint where a ridge divides to form two ridges. The Core as the name implies, is the approximate center of the finger Impression. 14, pp. 799813, 1996. Cybersecurity news and best practices are full of acronyms and abbreviations. Original language: English: Article number: 1850168: Journal: International Journal of Bifurcation and Chaos in Applied Sciences and Engineering . Cho, Fingerprint classification using one-vs-all support vector machines dynamically ordered with nave Bayes classifiers, Pattern Recognition, vol. The nervous system is also involved in this process. DermNet does not provide an online consultation service. 3, pp. The three fingerprint class types are arches, loops, and whorls. You can unsubscribe anytime. I wonder if theres any movement towards that. GoogleNet-22 architecture classifier results. Thanks a lot for sharing this post with us. 8594, 2004. A short ridge in a finger print looks like an island. Technology constraints in the early 2000s curtailed the learning ability of deep CNN architecture by limiting it to limited dimensions. endstream endobj 809 0 obj <>stream Ending Ridge Where the ridge of the fingerprint stops. From the results, we can observe that training accuracy rises to 100% in iterations 15, 69, 87, 99, and 102, which means that there is overfitting in training. However, these identifiers do not store any personal data. Results in primary and secondary ridge formation with anastomoses. A ridge is a high point. Our prints are as unique as snowflakes falling from a winter sky. Short ridges (or dot) are ridges which are significantly shorter than the average ridge length on the fingerprint. It basically means something divided or forked out. 3-4, pp. The third observation will be the points of identification. Thats my goal, anyway. L. Deng and Yu Dong, Deep learning: methods and applications, Foundations and Trends in Signal Processing, vol. Minutia and patterns are very important in the analysis of fingerprints. General Description of Fingerprints . Two innermost ridges which start parallel, diverge, and surround, or tend to surround, the Pattern Area. Bifurcation is also called ridge bifurcation. Binarization and thinning model has been used in order to improve image quality. I really like your blog as it is very helpful for those people who are new to this field like me. There is so much involved. 0 Want to create or adapt books like this? 838 0 obj <>stream Bush Les. Simulation of the strong arch fingerprint: (a) phase portrait of the model and (b) image of the strong arch fingerprint. 183198, 2017. This interesting related work could be integrated with this proposed method as future work and other many network [59, 60] problems to produce a new efficient method. Sir Francis Galtonwas the first person to classify fingerprints into different types based on the three basic features: Loops, Arches, and Whorls. And as for If a fingerprint has no Delta, well, at least it will get there on time. Friction ridge pattern that is found on the fingertips' skin. 12.2 Fingerprint Pattern and its elements -Arch (Plain and Tented . R. Gupta, M. Khari, D. Gupta, and R. Crespo, Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction, Informing Science, vol. It achieves a top-5 error rate of 3.57%, which beats human-level performance on this dataset. 799813, 1996. 02, pp. Afingerprintpattern type where the friction ridges make a complete circuit around the centre of the fingerprint. It presented the latest definition of the initiation block in CNN, where it integrates multiscale convolutionary transformations through the notion of separating, transforming, and merging. Examples of fingerprint patterns Loop Tented arch Whorl Double Loop Examples of minutiae Ridge ending Dot 863873, 2020. I got so much information at WPA, I have enough blog posts to last quite a while. There are three different types of whorls: plain whorl central pocket whorl double loop whorl and accidental whorl Each of these is different in its own way, but each type has two deltas. While there, I could hardly wait to shareall the things I learned with you. You beat me to the topic and did a great job. This is what makes them unique. In the experiment of the GoogleNet-22 that is applied to the proposed model, Table 3 shows the results that are obtained from training of the GoogleNet-22 architecture to the proposed model. These approaches are categorized based on rule-based, structural-based, frequency-based, and syntactic [6]. Loops are the most common, occurring 60-65% of the time. This pattern is an example of a first-level fingerprint detail. The CNN-based classifier has been implemented with varying layer numbers, and the GoogleNet has been implemented with 22 layers, where ResNet-18 has been implemented with 18 layers; finally, AlexNet has been implemented with 8 layers. For more details, review our Privacy Policy. The subset of each database consists of 80 fingerprints made available to the participants from 10 fingers. window.__mirage2 = {petok:"rf_oZ94KVZ6u1unLHFwGYMISsan1BIOTowOMk57rFjI-1800-0"}; What is a bifurcation fingerprint? One of the . By doing this, the time required for fingerprint identification is reduced. To solve automatic fingerprint identification, fingerprints can be stored in databases based on the characteristics of their ridge and furrow patterns. An uninterrupted friction ridge that continues until there is a ridge ending. The study by [11] implemented a fingerprint classification system using a fuzzy neural network classifier and its output in the recognition method. Together they form a unique fingerprint. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. Figure 4 shows the architecture of the proposed model. Which is why, Im guessing, most departments these days use live scanners to capture fingerprints rather than the ol roll in ink method. From the obtained results, GoogleNet provides better results in terms of accuracy. hVmoF+1t+t j+PY2$h}dZH<> AqB H` fY?8SGppqnPI This is incredibly useful! 201218, 2020. [13] proposed a combination of the SVM and the naive Bayes to classify the fingerprints based on the number of fingerprint core and delta points. 76, no. You wont want to miss this one! Trifurcation This looks like a pitch-fork and branches off into 3 ridge lines. Sue teaches a virtual course about serial killers for EdAdvance in CT and a condensed version for her fellow Sisters In Crime. 12.1 Fingerprint Terminology and Ridge Characteristics-Fingerprint Terminology (Ridge, Furrow, Pattern, Delta, Core, Latent Print, Ridge Tracing, Ridge Counting), Ridge characteristics (Ridge Ending, Bifurcation, Dot, Convergence, Divergence, Enclosure, Deviated Break etc.) It is observable that when the accuracy is high, the loss is low. W. Wang, Y. Yang, X. Wang, W. Wang, and J. Li, Development of convolutional neural network and its application in image classification: a survey, Optical Engineering, vol. If we examine a fingerprint, we need to study the Pattern Area the place where we can classify into type. Well delve deeper into Loops, Arches, and Whorls in a minute. Fingerprint matching techniques are divided into three main types: Input: Introduce fingerprint Image. If you haven't opted-in to my community, I will NOT add your name/email address to receive my newsletter and/or blog posts. GoogleNets emphasis was on making the CNN parameter effective in addition to increasing learning efficiency. Collectively, these features are called minutiae. 28, no. D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, Springer Science & Business Media, Berlin, Germany, 2009. Arches can be subdivided into two categories: tented arch and plain arch For a fingerprint to be considered an arch, the pattern must have ridges entering one side and exiting the other side. 8, pp. The bottom right fingerprint is a arch. Terminology: General Terms on Fingerprint Visualization. A chart illustrating fingerprint ridge patterns (arches, loops and whorls) and fingerprint ridge characteristics (core, ending ridge, short ridge, fork or bifurcation, delta, hook, eye, dot or island, crossover, bridge, enclosures, and speciality). In biometrics and fingerprint scanning, bifurcation refers to the point in a fingerprint where a ridge divides to form two ridges. After fingerprint classification is completed, fingerprint matching is required. Arch patterns can be further categorized into two subgroups: . According to Figure 21, the AlexNet is the faster one. end. Therefore, this research categorized the dataset into three main fingerprint patterns (arches, loops, and whorls) that make CNN works efficiently. Friction ridges can become thicker and shorter with ageing, and are more difficult to discern. Daniel B. Cox recently postedV-Juice Bar Container. The top right fingerprint is a whorl. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation . Most of the fingerprints datasets have a large size. To analyze the effect of the proposed model, different CNN architecture classifiers were applied. Recommended Reading: See Webopedias How Fingerprint Scanners Work and also learn How Biometrics Security Works. Although this is a process with defined steps, when making a . The embedded access points for trusted data and resources access in HPC systems [58] are one of interesting related work that discussed one possible solution for user authentication in network security and it is a hardware implementation in hight-performance computing field. Embarrassing doesnt come close to the right word. Yanin Chavarri-Guerra, M.D., and Enrique Soto-Perez-de-Celis, M.D. [52] as a novel architecture featuring skip connections and fast batch normalization. Its a lot harder than it looks. 348359, 1999. Due to its intraclass correlation diversities from the fingerprint images of the same finger and its correlation similarities from the fingerprint images of different fingers, fingerprint matching is a challenging pattern-recognition problem. This makes it individual evidence. The database of fingerprints can now be indexed based on one of these three classes [4]. arrow-right-small-blue Canadian court standards have long accepted 10 points of identification as an acceptable standard and have been known to convict on as little as 7 provided there is other corroborating evidence. SVM is an ML algorithm that adopts a robust approach to fingerprint classification. 'L''iBn$]FxGiCd\xo`8; ^OofO4(xC;9t H1fesM ()}kGglN`k\Y8UO}=cP{8& 1 All Rights Reserved Among them, the generally used features are orientation and singularities image information. C. O. Folorunso, O. S. Asaolu, and O. P. Popoola, A review of voice-base person identification:state-of-the-art, Covenant Journal of Engineering Technology (CJET), vol. Moreover, Hong and Wang et al. 02CH37291), Honolulu, USA, May 2002. There are two main theories that aim to explain the processes underlying friction ridge formation. "9Ph"uipX"tcF?M&E9/qmV},I.J`r8(z,P*78@+6. The model comprises of modules for fingerprint image noise attenuation, ridge contrast enhancement, map extraction, and filtering. ;Gn1vwXSf;~cgYZ0A[ZlW]L ~k*b,>%`p53IgW[GmE\c). Bit of trivia the known set of prints on a card is called a tenprint. Criminal courts require 8 to 12 minutiae to match. Individualistic details of various levels that are observed within afingerprint. Thinning makes it easier to find minutiae and removes a lot of redundant data that would have resulted in longer process time and sometimes different results [, https://doi.org/10.1109/CVPR.2015.7298594. 4642, 2007. (i)2D grayscale to 3D color: the main idea to convert from 2D grayscale to 3D color is that the CNNs just accept the image with 3D color so that the following MATLAB function is used for converting:AugmentedTrainingSet=augmentedImageDatastore(imageSize, trainingSet, ColorPreprocessing, gray2rgb); augmentedTestSet=augmentedImage-Datastore (imageSize, testSet, ColorPreprocessing, gray2rgb);(ii)The separation of the object and background is known as binarization. It is formed by a single friction ridge that bifurcates and rejoins after a short course and continues as a single friction ridge . Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. end. HMk@9~j! r9GVt Garry Rodgers recently postedCOURTROOM COMEDY GREAT LINES FROM GOOFY LAWYERS.
Glutinous Rice Flour Morrisons,
P320 Tungsten Grip Weight Kit,
Articles B