A DoG based Approach for Fingerprint Image Enhancement

Fingerprints have been the most accepted tool for personal identification since many decades. It is also an invaluable tool for law enforcement and forensics for over a century, motivating the research in Automated fingerprint-based identification, an application of biometric system. The matching or identification accuracy using fingerprints has been shown to be very high. The theory on the uniqueness of fingerprint minutiae leads to the steps in studying the statistics of extracting the minutiae features reliably. Fingerprint images obtained through various sources are rarely of perfect quality. They may be degraded or noisy due to variations in skin or poor scanning technique or due to poor impression condition. Hence enhancement techniques are applied on fingerprint images prior to the minutiae point extraction to get sure of less spurious and more accurate minutiae points from the reliable minutiae location. This thesis focuses on fingerprint image enhancement techniques through histogram equalization applied locally on the degraded image. The proposed work is based on the Laplacian pyramid framework that decomposes the input image into a number of band-pass images to improve the local contrast, as well as the local edge information. The resultant image is passed through the regular methodologies of fingerprint, like ridge orientation, ridge frequency calculation, filtering, binarization and finally the morphological operation thinning. Experiments using different texture of images are conducted to enhance the images and to show a comparative result in terms of number of minutiae extracted from them along with the spurious and actual number existing in each enhanced image. Experimental results out performs well to overcome the counterpart of enhancement technique

A study on fingerprint image enhancement and minutiae extraction techniques

Existing security measures rely on knowledge-based approaches like passwords or token based approaches such as swipe cards and passports to control access to physical and virtual spaces. Though ubiquitous, such methods are not very secure. Tokens such as badges and access cards may be shared or stolen. Furthermore, they cannot differentiate between authorized user and a person having access to the tokens or passwords. Biometrics such as fingerprint, face and voice print offers means of reliable personal authentication that can address these problems and is gaining citizen and government acceptance. Fingerprints were one of the first forms of biometric authentication to be used for law enforcement and civilian applications. Reliable extraction of features from poor quality prints is the most challenging problem faced in the area of fingerprint recognition. In this thesis, we introduce a new approach for fingerprint image enhancement based on the Gabor filter have been widely used to facilitate various fingerprint applications such as fingerprint matching and fingerprint classification. Gabor filters are band pass filters that have both frequency- selective and orientation-selective properties, which means the filters can be effectively tuned to specific frequency and orientation values. The proposed analysis and enhancement algorithm simultaneously estimates several intrinsic properties of the fingerprint such as the foreground region mask, local ridge orientation and local frequency. We also objectively measure the effectiveness of the enhancement algorithm and show that it can improve the sensitivity and recognition accuracy of existing feature extraction and matching algorithms. We also present a new feature extraction algorithm is the Crossing Number (CN) concept. This method involves the use of the skeleton image where the ridge flow pattern is eightconnected. The minutiae are extracted by scanning the local neighborhood of each ridge pixel in the image using a 3x3 window. The CN value is then computed, which is defined as half the sum of the differences between pairs of adjacent pixels in the eight-neighborhood. The algorithm has several advantages over the techniques proposed in literature such as increased computational efficiency, improved localization and higher sensitivity

Fingerprint recognition: A study on image enhancement and minutiae extraction

Fingerprints are a great source for identification of individuals. Fingerprint recognition is one of the oldest forms of biometric identification. However obtaining a good fingerprint image is not always easy. So the fingerprint image must be preprocessed before matching. The objective of this project is to present a better and enhanced fingerprint image. We have studied the factors relating to obtaining high performance feature points detection algorithm, such as image quality, segmentation, image enhancement and feature detection. Commonly used features for improving fingerprint image quality are Fourier spectrum energy, Gabor filter energy and local orientation. Accurate segmentation of fingerprint ridges from noisy background is necessary. For efficient enhancement and feature extraction algorithms, the segmented features must be void of any noise. A preprocessing method consisting of field orientation, ridge frequency estimation, Gabor filtering, segmentation and enhancement is performed. The obtained image is applied to a thinning algorithm and subsequent minutiae extraction. The methodology of image preprocessing and minutiae extraction is discussed. The simulations are performed in the MATLAB environment to evaluate the performance of the implemented algorithms. Results and observations of the fingerprint images are presented at the end

Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation

Im ersten Teil dieser Arbeit wird Fingerwachstum untersucht und eine Methode zur Vorhersage von Wachstum wird vorgestellt. Die Effektivität dieser Methode wird mittels mehrerer Tests validiert. Vorverarbeitung von Fingerabdrucksbildern wird im zweiten Teil behandelt und neue Methoden zur Schätzung des Orientierungsfelds und der Ridge-Frequenz sowie zur Bildverbesserung werden vorgestellt: Die Line Sensor Methode zur Orientierungsfeldschätzung, gebogene Regionen zur Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur Bildverbesserung. Multi-level Jugdment Aggregation wird eingeführt als Design Prinzip zur Kombination mehrerer Methoden auf mehreren Verarbeitungsstufen. Schließlich wird Score Neubewertung vorgestellt, um Informationen aus der Vorverarbeitung mit in die Score Bildung einzubeziehen. Anhand eines Anwendungsbeispiels wird die Wirksamkeit dieses Ansatzes auf den verfügbaren FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the thesis and a method for growth prediction is presented. The effectiveness of the method is validated in several tests. Fingerprint image preprocessing is discussed in the second part and novel methods for orientation field estimation, ridge frequency estimation and image enhancement are proposed: the line sensor method for orientation estimation provides more robustness to noise than state of the art methods. Curved regions are proposed for improving the ridge frequency estimation and curved Gabor filters for image enhancement. The notion of multi-level judgment aggregation is introduced as a design principle for combining different methods at all levels of fingerprint image processing. Lastly, score revaluation is proposed for incorporating information obtained during preprocessing into the score, and thus amending the quality of the similarity measure at the final stage. A sample application combines all proposed methods of the second part and demonstrates the validity of the approach by achieving massive verification performance improvements in comparison to state of the art software on all available databases of the fingerprint verification competitions (FVC)

Student Attendance System Based on Fingerprint Recognition and One to Many Matching

Our project aims at designing an student attendance system which could effectively manage attendance of students at institutes like NIT Rourkela. Attendance is marked after student identification. For student identification, a fingerprint recognition based identification system is used. Fingerprints are considered to be the best and fastest method for biometric identification. They are secure to use, unique for every person and does not change in one's lifetime. Fingerprint recognition is a mature field today, but still identifying individual from a set of enrolled fingerprints is a time taking process. It was our responsibility to improve the fingerprint identification system for implementation on large databases e.g. of an institute or a country etc. In this project, many new algorithms have been used e.g. gender estimation, key based one to many matching, removing boundary minutiae. Using these new algorithms, we have developed an identification system which is faster in implementation than any other available today in the market. Although we are using this fingerprint identification system for student identification purpose in our project, the matching results are so good that it could perform very well on large databases like that of a country like India (MNIC Project). This system was implemented in Matlab10, Intel Core2Duo processor and comparison of our one to many identification was done with existing identification technique i.e. one to one identification on same platform. Our matching technique runs in O(n+N) time as compared to the existing O(Nn^2). The fingerprint identification system was tested on FVC2004 and Verifinger databases