Tented Arch Research Articles

A dermatoglyphic study in oral submucous fibrosis patients

Aims and Objectives: (1) To determine the fingerprint patterns in gutkha chewers with oral submucous fibrosis (OSMF) and in healthy controls without gutkha chewing habit and OSMF. (2) To observe and compare the variations of fingerprints in both these groups. Materials and Methods: A prospective study was conducted in the Department of Oral Medicine and Radiology, Career Postgraduate Institute of Dental Sciences and Hospital, Lucknow, with 50 subjects divided into two groups: group A consisted of 25 clinically and histologically proven OSMF cases and group B consisted of 25 controls without gutkha chewing habit and OSMF. Fingerprints were taken using the ink method described by Cummins and Midlo. Statistical analysis was done using Chi-square and Fisher's exact tests. Results: There was a marked decrease of tented arches, ulnar and radial loops, and an increase of simple whorls in OSMF subjects in comparison with normal individuals. Conclusion: Fingerprint pattern is genetically determined. Similarly, the tendency of development of OSMF in gutkha chewers is also genetically determined. An effort was made to compare the fingerprint patterns among gutkha chewers with OSMF in a small sample, which showed that there was a significant decrease of tented arches, ulnar and radial loops, and an increase of simple whorls in OSMF patients in comparison with normal individuals.

A Study of Biometric Approach Using Fingerprint Recognition

Fingerprint (FP) serves to identify that the person authenticating is who he/she claims to be. FP identification is popular biometric technique due to easiness in acquiring, availability of plenty sources (i.e. ten fingers) for collecting data and their established use. This paper summarizes the research work carried out in FP matching techniques, recognition methods and their performance analysis. 0.001 to 0.01% of FAR (False Acceptance Rate). The pre-requisite to match the FP is classification. The classification is treated as course level matching. The FP can be classified as whorl, right loop, arch, tented arch. In order to ensure the performance of FP identification, enhancement algorithms are needed to improve clarity of input fingerprint images.

Study of finger print patterns in children with acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a malignant disease of an unknown origin. The present case control study was intended to compare the finger print patterns between the children diagnosed with ALL and a control group. A total of 24 children with established ALL were included in the study. Fingerprints of the affected children were analyzed in both hands and compared with the finger print patterns of 24 age and sex matched controls. The study indicated an increase in frequency of whorls and a decreased frequency of loops in children suffering from ALL. Radial loops, double loops, central pocket loops and tented arches are found to be proportionately less frequent in cases than controls. On quantitative analysis of patterns, mean pattern intensity index (PII) was found to be higher in cases than controls; however, a significant overlapping was evident. Most of the cases (n = 10, 41.7%) had a PII of 16-20, while a PII of 11-15 is reported among most children in the control group (n = 16, 66.7%). The findings of the study are suggestive of a possible trend and an association of finger print patterns with children suffering from ALL. The present research emphasizes the significance of the application of forensic science in medicine and pathology.

Singular points detection method in tented arch fingerprints

Singular points detection method in tented arch fingerprints

A DIRECTIONAL APPROACH TO FINGERPRINT CLASSIFICATION

In this article, we present a new fingerprint classification algorithm. Singular points are first extracted from enhanced fingerprint direction images with a resolution of 2 × 2 pixels by the modified SEA algorithm. Based on the number of singular points, fingerprints are categorized into types of "arch", "whorl", and "solitary". Solitary fingerprints are properly rotated and then further processed to generate direction patterns that lead to establishment of individual direction template. Direction constraints are formed and derived from pattern descriptors by their structural layout. Decision rules are then established and pattern templates are classified into three more types: "right loop", "left loop", and "tented arch". NIST-4 database was used for an experimental test, and our classification accuracy was 91.62% with 1.55% rejection on the five-class system (94.38% on the four-class system), which is the best result on the five-class system to-date. An additional experiment on NIST-14 database reports 89.15% accuracy with 3.07% rejection.

Application of a new type of singular points in fingerprint classification

The singular points, core and delta, are widely used in fingerprint classification. However a true pair of core and delta that are close to one another is often ignored. In this paper, we define a new type of singular point denoted by S CD for representing a pair of core and delta. A new algorithm based on the distribution of Gaussian–Hermite moments is used to detect S CD. With core, delta and S CD, the accuracy of fingerprint classification is improved, especially for tented arches. The proposed method has been tested on the NIST-4. We can improve the accuracy of algorithm (Zhang and Yan, 2004) [Zhang, Q., Yan, H., 2004. Fingerprint classification based on extraction and analysis of singularities and pseudo ridges. Pattern Recognit. 37, 2233–2243] by 26.7% for identifying tented arch, and the classification accuracy can be improved by 4.3% for five-class problem.

Fingerprint classification using a feedback-based line detector.

We present a fingerprint classification algorithm in this paper. This algorithm classifies a fingerprint image into one of the five classes: Arch, Left loop, Right loop, Whorl, and Tented arch. We use a new low-dimensional feature vector obtained from the output of a novel oriented line detector presented here. Our line detector is a co-operative dynamical system that gives oriented lines and preserves multiple orientations at points where differently oriented lines meet. Our feature extraction process is based on characterizing the distribution of orientations around the fingerprint. We discuss three different classifiers: support vector machines, nearest-neighbor classifier, and neural network classifier. We present results obtained on a National Institute of Standards and Technology (NIST) fingerprint database and compare with other published results on NIST databases. All our classifiers perform equally well, and this suggests that our novel line detection and feature extraction process indeed captures all the crucial information needed for classification in this problem.

융선 엔트로피 계측을 이용한 지문 분류

지문의 분류(Classification)는 대용량 지문 데이터베이스에서 정합시간의 단축과 정확도를 높여주는 역할을 한다. 지문의 종류는 크게 궁상문, 솟은 궁상문, 좌제상문, 우제상문, 와상문의 5종류로 분류되며, 이는 중심점과 삼각점의 개수 및 위치등을 이용하여 분류하고 있다. 기존의 지문 분류는 중심점과 삼각점을 모두 획득하는 회전날인의 경우에 사용 가능한 분류방법이나 현대의 자동화된 실시간 지문인식 시스템에서는 입력센서의 크기 및 입력방법의 문제등으로 인하여 적용할 수 없는 문제점이 있다. 본 논문에서는 이러한 문제점을 해결하기 위하여, 중심점을 획득한 지문을 이용하여 중심점에서 융선의 엔트로피 계측을 기반으로 하며 지문 분류의 정확성을 향상시킬 수 있는 방법을 제안하고 실험을 통하여 이를 증명한다. Fingerprint classification plays a role of reduction of precise joining time and improvement of the accuracy in a large volume of database. Patterns of fingerprint are classified as 5 patterns : left loop, right loop, arch, whorl, and tented arch by numbers and the location of core point and delta point. The existing fingerprint classification is useful in a captured fingerprint image of core point and delta point using paper and ink. However, this system is unapplicable in modern Automatic Fingerprint Identification System (AFIS) because of problems such as size of input and way of input. To solve the problem, this study is to suggest the way of being able to improve accuracy of fingerprint by fingerprint classification based on the entropy of ridges using fingerprint captured mage of core point and prove this through the experiment.

Fingerprints classification using artificial neural networks: a combined structural and statistical approach

This paper describes a fingerprint classification algorithm using Artificial Neural Networks (ANN). Fingerprints are classified into six categories: arches, tented arches, left loops, right loops, whorls and twin loops. The algorithm extracts a string of symbols using the block directional image of a fingerprint, which represents the set of structural features for this image. The moment representing the statistical feature of the pattern is computed for this string and its Euclidean Distance Measures (EDM) are computed by using this moment. Our discrimination system uses a multilayer artificial neural network composed of six subnetworks one for each class. The classifier was tested on 1500 images of good quality in the Egyptian Fingerprints database; images with poor quality were rejected. In the six-class problem the network achieved 95% classification accuracy. In the five-class problem when we place whorls and twin loops together in the same category the classification accuracy was around 99%. In the four-class problem when we place arches and tented arches in the same class the classification accuracy was 99%.

A multichannel approach to fingerprint classification

Fingerprint classification provides an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for a large database. We present a fingerprint classification algorithm which is able to achieve an accuracy better than previously reported in the literature. We classify fingerprints into five categories: whorl, right loop, left loop, arch, and tented arch. The algorithm uses a novel representation (FingerCode) and is based on a two-stage classifier to make a classification. It has been tested on 4000 images in the NIST-4 database. For the five-class problem, a classification accuracy of 90 percent is achieved (with a 1.8 percent rejection during the feature extraction phase). For the four-class problem (arch and tented arch combined into one class), we are able to achieve a classification accuracy of 94.8 percent (with 1.8 percent rejection). By incorporating a reject option at the classifier, the classification accuracy can be increased to 96 percent for the five-class classification task, and to 97.8 percent for the four-class classification task after a total of 32.5 percent of the images are rejected.

Fingerprint classification

A fingerprint classification algorithm is presented in this paper. Fingerprints are classified into five categories: arch, tented arch, left loop, right loop and whorl. The algorithm extracts singular points (cores and deltas) in a fingerprint image and performs classification based on the number and locations of the detected singular points. The classifier is invariant to rotation, translation and small amounts of scale changes. The classifier is rule-based, where the rules are generated independent of a given data set. The classifier was tested on 4000 images in the NIST-4 database and on 5400 images in the NIST-9 database. For he NIST-4 database, classification accuracies of 85.4% for the five-class problem and 91.1% for the four-class problem (with arch and tented arch placed in the same category) were achieved. Using a reject option, the four-class classification error can be reduced to less than 6% with 10% fingerprint images rejected. Similar classification performance was obtained on the NIST-9 database.

Twin study of digital dermatoglyphic traits: Investigation of heritability.

Digital patterns of a sample of monozygotic and dizygotic twins were analyzed to obtain heritability values for the four basic types of patterns: arch, tented arch, loops, and whorls. Loops have been separated according to their orientation radial or ulnar. To carry out this study, we used the Holzinger and Clark indices. The results show highest concordances for monozygotic twins except for ulnar loops of the left hand and radial ones of the right hand. Different values of heritability were found for the right and left hands, with finger I having the highest values.

Digital dermatoglyphics of Bulgarians from northeast Bulgaria.

Digital dermatoglyphics were collected from 1,065 male and 1,065 female Bulgarians from northeast Bulgaria. None of the subjects had a diagnosed or suspected genetic or chronic disease of any kind. The fingerprints were classified by the 18-type system of Monique de Lestrange, modified to provide rapid and easy comparison with simpler classification systems. All the standard finger pattern indices were calculated. Certain modifications were introduced into the delta indices, it being borne in mind that each tented arch possesses a triradius and each complex (three-centered, accidental) whorl contains three triradii. A deltadiagram was constructed and its configuration was compared with those of some other populations. In addition, a new radioulnar index was proposed, representing a ratio between all the radial and all the ulnar patterns. The total, absolute, ulnar and radial finger ridge counts were calculated and their sample distributions were investigated. The dermatoglyphic features were evaluated and presented for each sex and each hand separately in order to investigate both the sex and bilateral differences. The set of data presented in this paper is a component of the physical anthropology of the general Bulgarian population. At the same time these data can be used as controls when analyzing the dermatoglyphic findings in Bulgarian patients with genetic diseases or congenital malformations.

Dermatoglyphic studies of myocardial infarction patients.

Dermatoglyphic traits were studied in a sample of 834 subjects selected from a cohort of some 8,000 living Japanese men, under a long-term study of heart disease in Hawaii. All of them were born between 1900 and 1919. Among them, 100 subjects had had positive diagnosis of myocardial infarction (MI). The present study included comparisons between the MI patients and the remaining group of all digital dermal pattern types and ridge counts by digit, by hand, and by individual. The MI patients had significantly higher frequency of true whorls, double loops and less ulnar loops and tented arches. Total and absolute ridge counts were significantly higher (less than 0.05) in all digits in favor of the MI patients. Similar trends were observed in analyses by digit and by hand. These observations suggest an antenatal origin of certain types of coronary disease.

Research Items

Finger-prints of Twins.-A study of the fingerprints of twins has been made by Prof. H. H. Newman (Jour. Genet., vol. 23, No. 3). The material consisted of the prints of 100 pairs of same-sexed twins, 50 of whom were classed as identical and 50 as di-zygotic or fraternal. He regards the whorl as the most primitive pattern, while some have given this place to the loop, which occurs in primitive form in anthropoids. He finds that the finger-prints of identical twins may be extraordinarily alike in pattern, although they always differ in minutiae. The distribution of whorls, loops, and arches is the same in both types of twins and agrees with that of the general population. Radial loops and radial whorls are largely confined to the index finger. This is interpreted as a result of the early dichotomy of the limb bud, separating the thumb primordium from the rest of the fingers and tending to produce in the index finger a pattern the reverse of that in the thumb. Tented arches, which are common on forefingers, are interpreted as cases of partial asymmetry reversal. In identical twins the patterns on one or both hands resemble the hands of the other twin more strongly than do opposite hands of the same individual. The reverse is true of fraternal twins, and this difference can be used as a criterion for classifying doubtful twin pairs. If the finger patterns in human twins are compared with the scale patterns in armadillos, it is found that in both man and the armadillo parallel-imaging is about twice as frequent as mirror-imaging of asymmetrical peculiarities. This leads to the conclusion that monozygotic twins in man arise in the same general way as the quadruplets which normally occur in the armadillo, that is, by a process of budding in the early embryo.