Error propagation in pattern recognition systems: Impact of quality on fingerprint categorization

The aspect of quality in pattern classification has recently been explored in the context of biometric identification and authentication systems. The results presented in the literature indicate that incorporating information about quality of the input pattern leads to improved classification performance. The quality itself, however, can be defined in a number of ways, and its role in the various stages of pattern classification is often ambiguous or ad hoc. In this dissertation a more systematic approach to the incorporation of localized quality metrics into the pattern recognition process is developed for the specific task of fingerprint categorization. Quality is defined not as an intrinsic property of the image, but rather in terms of a set of defects introduced to it. A number of fingerprint images have been examined and the important quality defects have been identified and modeled in a mathematically tractable way. The models are flexible and can be used to generate synthetic images that can facilitate algorithm development and large scale, less time consuming performance testing. The effect of quality defects on various stages of the fingerprint recognition process are examined both analytically and empirically. For these defect models, it is shown that the uncertainty of parameter estimates, i.e. extracted fingerprint features, is the key quantity that can be calculated and propagated forward through the stages of the fingerprint classification process. Modified image processing techniques that explicitly utilize local quality metrics in the extraction of features useful in fingerprint classification, such as ridge orientation flow field, are presented and their performance is investigated

Algorithm for Fingerprint Verification System

Extraction of minutiae based features from good quality fingerprint images is more effective for fingerprint recognition in comparison with features from low quality fingerprint. In this paper, a new technique for fingerprint feature extraction based on ridge pattern is proposed. Robust features are extracted from fingerprint image notwithstanding the quality of the image. The variation within different person fingerprint is established using centre of gravity of the fingerprint image as the reference point for effective classification. Similarity measure in term of Euclidean distance is compute for test fingerprint image

Profil Sidik Jari Populasi Etnis Manggarai Barat Di Labuan Bajo, Nusa Tenggara Timur, Indonesia

The aims of this study was to identify the fingerprint patterns of the West Manggarai ethnic in Labuan Bajo, East Nusa Tenggara, Indonesia. This type of research is descriptive-qualitative with a population of 70 original West Manggarai ethnic people. Data collection was carried out by interviewing and filling out information sheets and respondent's approval, then fingerprint patterns were printed. The fingerprint results were agreed by using a fingerprint classification system. The results is the fingerprint patterns in the West Manggarai ethnic group generally have an ulnar loop dominant pattern of 59.14% (414 fingers) and the second dominant pattern plain whorl with a total of 32.71% (229 fingers). The ulnar loop dominant pattern is found on the middle finger (M) and little finger (L), while the second dominant plain whorl pattern is located on the right thumb (T), left index finger (I), and both ring fingers (R)

An Algorithm for Fingerprint Classification Using Template Matching Technique

Automatic fingerprint classification has received considerable attention over the past decade. Despite significant progress in this field, there are still rooms for improving the classification operation by continuing study and research in this field. This thesis describes a study of fingerprint classification using template matching technique. We have classified the fingerprints in four groups according to their pattern, which are Arch, Left loop, Right loop, and Whorl. We have discussed and explained the specification and the limitations of the fingerprint classification (the effect of corrupted and rotated input fingerprints on the accuracy of the classification operation). The thesis has analysed the mentioned technique and evaluated its strengths and limitation by comparing this technique with the singularities technique. This research has also included the pre-processing stage, which consist of enhancement, segmentation, and thinning of fingerprints

Development of an Improved Fingerprint Feature Extraction Algorithm for Personal Verification

New and sophisticated technologies are regularly developed to counter every new wave of breaches in data security. At the heart of some of these technologies is the personal verification system that rests on the oars of biometrics. Biometric systems use unique physical and behavioral traits for identification or verification. In this paper, an improved fingerprint feature extraction algorithm for personal verification is proposed. The improved fingerprint feature extraction algorithm is capable of recognizing authorized individuals and differentiating them from fraudulent imposters. The input images were preprocessed before extracting robust features for matching. Euclidean distance was used for classification. The proposed system was tested using the fingerprint images of fifty registered individuals and thirty imposters. The results obtained were a False Acceptance Rate and False Rejection Rate of 16% and 24% respectively. It is also faster than other feature extraction algorithms by forty (40) seconds Keywords: Fingerprint, biometrics, robust features, division into blocks, ridge pattern, euclidean distance, personal verification, feature extraction, classification

Determination of vitality from a non-invasive biomedical measurement for use in integrated biometric devices

Personal identification is a very important issue in today\u27s mobile and electronically networked societies. Among the available measures, fingerprints are the oldest and most widely used. Unfortunately, it is usually possible to deceive automatic fingerprint identification systems by presenting a well-duplicated synthetic or dismembered finger. This project is one method to provide fingerprint vitality authentication in order to solve this problem. Using a sensor that is composed of an array of capacitors, this method identifies the vitality of a fingerprint by detecting a specific changing pattern over the human skin. Mapping the two-dimensional images into one-dimensional signals, two ensembles of measures, namely static and dynamic measures, are used for classification. Static patterns as well as temporal changes in dielectric mosaic structure of the skin demonstrate themselves in these signals. Using these measures, this algorithm quantifies this specific pattern and makes a final decision about vitality of the fingerprint by a neural network trained by examples

Profil Fingerprinting (Sidik Jari) pada Populasi Suku Ububewi Di Wanukaka Sumba Barat, Nusa Tenggara Timur, Indonesia

Fingerprints have unique and permanent properties as differentiators from one individual to another, even in identical twins the fingerprints are not the same, besides that fingerprints can be used as a search tool. This study aims to analyze fingerprint patterns in the population of the Ububewi tribe. This research is a qualitative descriptive study with a population of 70 students from the Ububewi tribe, West Sumba, East Nusa Tenggara. Data collection techniques were carried out by interviewing, filling out questionnaires, and printing fingerprints on reading sheets. The results of the fingerprint pattern were analyzed using the guidelines in the finger classification system. Based on the research, it shows that the characteristic of the fingerprint pattern of the Ububewi tribe is having a dominant ulnar loop (UL) pattern on the middle finger (M) of the right hand by 64.28% (45 fingers) and the little finger (L) of the right and left hands of 72. 86% (51 fingers) and 70% (49 fingers).   &nbsp

Classification Of Gender Using Global Level Features In Fingerprint For Malaysian Population

A new approach of algorithm based on the Mark Acree’s theory, focusing on fingerprint global extracted features is proposed and implemented for enhancing gender classification method. This proposed method can automatically execute the ridge calculation process from the 25mm2 fingerprint and enhance the forensic gender classification process. In this study, a relationship between fingerprint global features and a gender of person in Malaysian population is also explored, enhanced and improved by exploiting another five additional fingerprint features. A sample of 3000 fingerprints from 300 respondents of random selection are carefully taken before any relationship can be determined. For the classification part, five extracted features of the fingerprint are used which are Ridge Density (RD), Mean Ridge Count (RC), Ridge Thickness to Valley Thickness Ratio (RTVTR), White Lines Count (WLC) and Mean Pattern Types (PT). Two classification approaches which are the descriptive statistical and data mining are used in order to examine the classification of the gender by using the five extracted features. For data mining classification part, there are four popular machine learning classifiers used which are Bayesian Net.work (Bayes Net.), Multilayer Perceptron Neural Network (MLPNN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). These four classifiers are used in the data mining task with five test cases each in order to find the accuracies of the gender classification. The accuracy of the results from the proposed method is compared to the Acree Method is shown in terms of relative error. For statistical approach using Ridge Density (RD), the relative error is 3.7% for male respondent and 4.1% for female respondent. Meanwhile, the overall performance of the result from the proposed method achieved more than 90% classification rate for all the classifiers. SVM emerges as the best classifier for all the different cases in order to classify the gender using the results from the proposed method

Sexual dimorphism in the lip size and finger pattern by digital method- A cross-sectional study

Background: Human lips recognition is most intriguing and growing method in human identification. The lip prints are unique among individuals and shown to have a prospective role in sex identification. The fingerprints of an individual can be used in instances like criminological, civil cases due to their inimitable property for absolute identity. The study aims to identify fingerprint pattern and lip size for identification of gender.Methods: This study involved 100 dentistry students from our college (50 male and 50 female).The thumb, lip photos were recorded by using a digital camera. The lip size was calculated by using Adobe photoshop software. The fingerprints patterns were by read by using the classification given by Michael and Kucken's.Results: Males had more lip length and width when compared to females which are statistically significant(p=0.000). The predominant fingerprint pattern in the entire study was loop (67%) followed by whorl (23%) and then arch (10%). The arch pattern of fingerprint showed a statistical significance between males and females (p=0.008).Conclusion: In the present study, lip length, lip width and fingerprint pattern showed a significant difference in males and females which can be used to determine an individual's gender. Moreover, employing digital method in analysing the fingerprints and lip-prints is very convenient in terms of accessibility and storage.Keywords: Cheiloscopy; Forensic identification; Lip size