Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods

A preliminary approach to intelligent x-ray imaging for baggage inspection at airports

Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted

The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.Comment: 32 pages, 21 figure

A Comparative Analysis of Fingerprint Enhancement Technique through Absolute Mean Brightness Error and Entropy

An enhancement algorithm is applied on the i nput fingerprint image to improve the image quality and to repair broken ridges. There are various enhancement schemes used for enhancing an image which includes gray scales manipulation, Histogram Equalization, Fast Fourier Transformed enhancement and Gabor Filter. There are several factors that affect the quality of the acquired fingerprint image via presence of scars, variations of the pressure between the finger, worn artifacts and acquisition sensor etc. This paper shows the work performed on the new database of fingerprint image acquired with a 500dpi optical sensor. Three different enhancement algorithms are applied on the input image and the qualities of the reconstructed image are compared using Absolute mean brightness error, peak–signal to noiseratio and Entrop

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)

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