Image enhancement and segmentation on simultaneous latent fingerprint detection

A simultaneous latent fingerprint (SLF) image consists of multi-print of individual fingerprints that is lifted from a surface, typically at the crime scenes. Due to the nature and the poor quality of latent fingerprint image, segmentation becomes an important and very challenging task. This thesis presents an algorithm to segment individual fingerprints for SLF image. The algorithm aim to separate the fingerprint region of interest from image background, which identifies the distal phalanx portion of each finger that appears in SLF image. The algorithm utilizes ridge orientation and frequency features based on block-wise pixels. A combination of Gabor Filter and Fourier transform is implemented in the normalization stage. In the pre-processing stage, a modified version of Histogram equalization is proposed known as Alteration Histogram Equalization (AltHE). Sliding windows are applied to create bounding boxes in order to find out the distal phalanges region at the segmentation stage. To verify the capability of the proposed segmentation algorithm, the segmentation results is evaluated in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region. The ground truth foreground refers to the manual mark up region of interest area. In order to evaluate the performance of this method, experiments are performed on the Indian Institute of Information Technology Database- Simultaneous Latent Fingerprint (IIITD-SLF). Using the proposed algorithm, the segmented images were supplied as the input image for the matching process via a state art of matcher, VeriFinger SDK. Segmentation of 240 images is performed and compared with manual segmentation methods. The results show that the proposed algorithm achieves a correct segmentation of 77.5% of the SLF images under test

Image Processing and Features Extraction of Fingerprint Images

Several fingerprint matching algorithms have been developed for minutiae or template matching of fingerprint templates. The efficiency of these fingerprint matching algorithms depends on the success of the image processing and features extraction steps employed. Fingerprint image processing and analysis is hence an essential step to the efficient matching and classification of fingerprint features. To demonstrate the importance of the image processing of fingerprint images prior to image enrolment or comparison, the set of fingerprint images in databases (a) and (b) of the FVC (Fingerprint Verification Competition) 2000 database were analyzed using a features extraction algorithm. This paper presents the results of the features extraction of the datasets of the FVC 2000 database. It also discusses the limitations of the FVC database and recommends what can be done to improve proprietary databases

Système automatique de reconnaissance d'empreintes digitales. Sécurisation de l'authentification sur carte à puce

La reconnaissance d'empreintes digitales est une technique biométrique mature pour toute application d'identification ou de vérification d'individus. Dans cet article, nous décrivons la conception et le développement d'un système automatique d'authentification d'identité par empreintes digitales. Ce système automatique de reconnaissance d'empreintes digitales est basé sur une série d'algorithmes complexes apparentés aux domaines du traitement d'images et/ou de la reconnaissance de motifs (nuages de points). Son originalité repose sur le portage de la phase de comparaison sur une carte à puce SmartJ™ 32-bit pour assurer une authentification rapide et sécurisée

Fingerprint minutiae filtering based on multiscale directional information

Automatic identification of humans based on their fingerprints is still one of the most reliable identification methods in criminal and forensic applications, and is widely applied in civil applications as well. Most automatic systems available today use distinctive fingerprint features called minutiae for fingerprint comparison. Conventional feature extraction algorithm can produce a large number of spurious minutiae if fingerprint pattern contains large regions of broken ridges (often called creases). This can drastically reduce the recognition rate in automatic fingerprint identification systems. We can say that for performance of those systems it is more important not to extract spurious (false) minutia even though it means some genuine might be missing as well. In this paper multiscale directional information obtained from orientation field image is used to filter those spurious minutiae, resulting in multiple decrease of their number

ZEKİ BİR OTOMATİK PARMAKİZİ TANIMA SİSTEMİ TASARIMI VE GERÇEKLEŞTİRİLMESİ

This work presents an intelligent automatic fingerprint identification and verification system based on Artificial Neural Networks (ANNs). In this work, the design processes of the system have been presented step by step. Fingerprints were first converted into digital images using a specific hardware. They were then processed by a computer. Fingerprint images were divided into grid blocks, and these blocks were classified as image area and background. An effective algorithm was used to detect the fingerprint singularities from gray level fingerprint images. In order to improve the performance of the system, fingerprint image enhancement was performed by using ANN. The adaptive backpropagation with momentum learning algorithm was used to train the ANN models. Binary images were obtained from the enhancement images using a regional binarization algorithm. Binary images were converted to thinned images. Ridge endings and ridge bifurcations of the fingerprints (minutiae) were extracted. A postprocessing algorithm was used to eliminate false minutiae patterns and the fingerprint matching process was finally applied. In order to automatise the system, a software for fingerprint identification and verification was developed in Delphi. The system developed in this work was tested 100 fingerprint images for identification and verification; it achieves the task with high accuracy. It is assumed that the developed system can be used in many security applications.Bu çalışmada, yapay sinir ağları (YSA) destekli zeki bir otomatik parmakizi tanıma sistemi başarıyla geliştirilmiş ve sunulmuştur. Sistem tasarlanırken işlemler adım adım yapılmıştır. Öncelikle bir parmakizi okuyucu yardımıyla alınan parmakizi resimleri sayısala çevrilmiştir. Resimler küçük parçalara bölünerek üzerinde işlem yapılacak alan arkaplandan ayrılmıştır. Gri seviye resimlerden referans noktalar elde edilmiştir. Parmakizi temizleme ve iyileştirme için YSA modeli geliştirilmiş, iyi sonuç veren momentumlu geriyayılım öğrenme algoritması kullanılarak bu model eğitilmiştir. Temizlenip iyileştirilen resimlere bölgesel ikili dönüşüm uygulanmış ve daha sonra siyah beyaz renkten oluşan ikili resim inceltilmiştir. İnceltilmiş resim üzerinde özellik noktaları olarak adlandırılan uç ve çatal noktalar ve bunlarla ilgili gerekli parametreler bulunmuş ve yalancı özellik noktaları elenmiştir. Son olarak karşılaştırma algoritması belirlenip karşılaştırma işlemi yapılmıştır. Sunulan çalışmada, belirtilen tüm adımlar başarıyla tamamlanmış ve bu işlemlerin kolaylıkla yapılabilmesi için Delphi programlama ortamında bir yazılım geliştirilmiştir. Hem tanıma, hem de onaylama/doğrulama modunda çalışabilen sistem, 100 parmakizi resminin bulunduğu bir veritabanında test edilmiş ve başarılı sonuçlar elde edilmiştir

Generating One Biometric Feature from Another: Faces from Fingerprints

This study presents a new approach based on artificial neural networks for generating one biometric feature (faces) from another (only fingerprints). An automatic and intelligent system was designed and developed to analyze the relationships among fingerprints and faces and also to model and to improve the existence of the relationships. The new proposed system is the first study that generates all parts of the face including eyebrows, eyes, nose, mouth, ears and face border from only fingerprints. It is also unique and different from similar studies recently presented in the literature with some superior features. The parameter settings of the system were achieved with the help of Taguchi experimental design technique. The performance and accuracy of the system have been evaluated with 10-fold cross validation technique using qualitative evaluation metrics in addition to the expanded quantitative evaluation metrics. Consequently, the results were presented on the basis of the combination of these objective and subjective metrics for illustrating the qualitative properties of the proposed methods as well as a quantitative evaluation of their performances. Experimental results have shown that one biometric feature can be determined from another. These results have once more indicated that there is a strong relationship between fingerprints and faces

Skeleton-based fingerprint minutiae extraction.

by Zhao Feng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 64-68).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.viTable of Contents --- p.viiList of Figures --- p.ixList of Tables --- p.xChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Automatic Personal Identification --- p.1Chapter 1.2 --- Biometrics --- p.2Chapter 1.2.1 --- Objectives --- p.2Chapter 1.2.2 --- Operational Mode --- p.3Chapter 1.2.3 --- Requirements --- p.3Chapter 1.2.4 --- Performance Evaluation --- p.4Chapter 1.2.5 --- Biometric Technologies --- p.4Chapter 1.3 --- Fingerprint --- p.6Chapter 1.3.1 --- Applications --- p.6Chapter 1.3.2 --- Advantages of Fingerprint Identification --- p.7Chapter 1.3.3 --- Permanence and Uniqueness --- p.8Chapter 1.4 --- Thesis Overview --- p.8Chapter 1.5 --- Summary --- p.9Chapter Chapter 2 --- Fingerprint Identification --- p.10Chapter 2.1 --- History of Fingerprints --- p.10Chapter 2.2 --- AFIS Architecture --- p.12Chapter 2.3 --- Fingerprint Acquisition --- p.15Chapter 2.4 --- Fingerprint Representation --- p.16Chapter 2.5 --- Fingerprint Classification --- p.18Chapter 2.6 --- Fingerprint Matching --- p.20Chapter 2.7 --- Challenges --- p.21Chapter 2.8 --- Combination Schemes --- p.22Chapter 2.9 --- Summary --- p.23Chapter Chapter 3 --- Live-Scan Fingerprint Database --- p.24Chapter 3.1 --- Live-Scan Fingerprint Sensors --- p.24Chapter 3.2 --- Database Features --- p.24Chapter 3.3 --- Filename Description --- p.28Chapter Chapter 4 --- Preprocessing for Skeleton-Based Minutiae Extraction --- p.30Chapter 4.1 --- Review of Minutiae-based Methods --- p.31Chapter 4.2 --- Skeleton-based Minutiae Extraction --- p.32Chapter 4.2.1 --- Preprocessing --- p.33Chapter 4.2.2 --- Validation of Bug Pixels and Minutiae Extraction --- p.40Chapter 4.3 --- Experimental Results --- p.42Chapter 4.4 --- Summary --- p.44Chapter Chapter 5 --- Post-Processing --- p.46Chapter 5.1 --- Review of Post-Processing Methods --- p.46Chapter 5.2 --- Post-Processing Algorithms --- p.47Chapter 5.2.1 --- H-Point --- p.47Chapter 5.2.2 --- Termination/Bifurcation Duality --- p.48Chapter 5.2.3 --- Post-Processing Procedure --- p.49Chapter 5.3 --- Experimental Results --- p.52Chapter 5.4 --- Summary --- p.54Chapter Chapter 6 --- Conclusions and Future Work --- p.58Chapter 6.1 --- Conclusions --- p.58Chapter 6.2 --- Problems and Future Works --- p.59Chapter 6.2.1 --- Problem 1 --- p.59Chapter 6.2.2 --- Problem 2 --- p.61Chapter 6.2.3 --- Problem 3 --- p.61Chapter 6.2.4 --- Future Works --- p.62Bibliography --- p.6

Extracting fingerprint features using textures

Personal identification of individuals is becoming increasingly adopted in society today. Due to the large number of electronic systems that require human identification, faster and more secure identification systems are pursued. Biometrics is based upon the physical characteristics of individuals; of these the fingerprint is the most common as used within law enforcement. Fingerprint-based systems have been introduced into the society but have not been well received due to relatively high rejection rates and false acceptance rates. This limited acceptance of fingerprint identification systems requires new techniques to be investigated to improve this identification method and the acceptance of the technology within society. Electronic fingerprint identification provides a method of identifying an individual within seconds quickly and easily. The fingerprint must be captured instantly to allow the system to identify the individual without any technical user interaction to simplify system operation. The performance of the entire system relies heavily on the quality of the original fingerprint image that is captured digitally. A single fingerprint scan for verification makes it easier for users accessing the system as it replaces the need to remember passwords or authorisation codes. The identification system comprises of several components to perform this function, which includes a fingerprint sensor, processor, feature extraction and verification algorithms. A compact texture feature extraction method will be implemented within an embedded microprocessor-based system for security, performance and cost effective production over currently available commercial fingerprint identification systems. To perform these functions various software packages are available for developing programs for windows-based operating systems but must not constrain to a graphical user interface alone. MATLAB was the software package chosen for this thesis due to its strong mathematical library, data analysis and image analysis libraries and capability. MATLAB enables the complete fingerprint identification system to be developed and implemented within a PC environment and also to be exported at a later date directly to an embedded processing environment. The nucleus of the fingerprint identification system is the feature extraction approach presented in this thesis that uses global texture information unlike traditional local information in minutiae-based identification methods. Commercial solid-state sensors such as the type selected for use in this thesis have a limited contact area with the fingertip and therefore only sample a limited portion of the fingerprint. This limits the number of minutiae that can be extracted from the fingerprint and as such limits the number of common singular points between two impressions of the same fingerprint. The application of texture feature extraction will be tested using variety of fingerprint images to determine the most appropriate format for use within the embedded system. This thesis has focused on designing a fingerprint-based identification system that is highly expandable using the MATLAB environment. The main components that are defined within this thesis are the hardware design, image capture, image processing and feature extraction methods. Selection of the final system components for this electronic fingerprint identification system was determined by using specific criteria to yield the highest performance from an embedded processing environment. These platforms are very cost effective and will allow fingerprint-based identification technology to be implemented in more commercial products that can benefit from the security and simplicity of a fingerprint identification system

Reconhecimento de impressões digitais com baixo custo computacional para um sistema de controle de acesso

Orientador: Eduardo Parente RibeiroDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 2005Inclui bibliografi