Pauli Kuosmanen

Fingerprint matching using an orientation-based minutia descriptor

We introduce a novel fingerprint representation scheme that relies on describing the orientation field of the fingerprint pattern with respect to each minutia detail. This representation allows the derivation of a similarity function between minutiae that is used to identify corresponding features and evaluate the resemblance between two fingerprint impressions. A fingerprint matching algorithm, based on the proposed representation, is developed and tested with a series of experiments conducted on two public domain collections of fingerprint images. The results reveal that our method can achieve good performance on these data collections and that it outperforms other alternative approaches implemented for comparison.

Fingerprint recognition using wavelet features

A new method of fingerprint recognition based on features extracted from the wavelet transform of the discrete image is introduced. The wavelet features are extracted directly from the gray-scale fingerprint image with no pre-processing (i.e. image enhancement, directional filtering, ridge segmentation, ridge thinning and minutiae extraction). The proposed method has been tested on a small fingerprint database using the k-nearest neighbor (k-NN) classifier. The very high recognition rates achieved show that the proposed method may constitutes an efficient solution for a small-scale fingerprint recognition system.

Soft morphological filtering

Stack filters are widely used nonlinear filters based on threshold decomposition and positive Boolean functions. They have shown to form a very large class of filters which includes rank-order operations as well as standard morphological operations. The stack filter representation of an order statistic filter provides an efficient tool for the theoretical analysis of the filter.Soft morphological filters form a large subclass of stack filters. They were introduced to improve the behavior of standard morphological filters in noisy conditions. In this paper, different properties of soft morphological filters are analysed and illustrated. Their connection to stack filters is established, and that connection is used in the statistical analysis of soft morphological filters. Soft morphological filters are less sensitive to additive noise than standard morphological filters. The deterministic properties of soft morphological filters are also analysed and it is shown that soft morphological filters form a class of filters with many desirable properties. For example, they preserve well details of images.

An algorithm for fingerprint image postprocessing

Most of the current fingerprint identification and verification systems performs fingerprint matching based on different attributes of the minutia details present in fingerprints. The minutiae (i.e. ridge endings and ridge bifurcations) are usually detected in the thinned binary image of the fingerprint. Due to the presence of noise as well as the use of different preprocessing stages the thinned binary image contains a large number of false minutiae which may highly decrease the matching performance of the system. A new algorithm of fingerprint image postprocessing is proposed. The algorithm operates onto the thinned binary image of the fingerprint in order to eliminate the false minutiae. The proposed algorithm is able to detect and cancel the minutiae associated with most of the false minutia structures which may be encountered in the thinned fingerprint image.

A transform domain LMS adaptive filter with variable step-size

We introduce a new transform domain (least mean square) LMS algorithm with variable step. The existing approaches use different time-variable step-sizes for each filter tap. The step-sizes are time-variable due to the power estimates of each transform coefficient. In our new approach, for each step-size we define a local component that is given by the power normalization, and a global component that is the same for each filter coefficient. We show that if the global component is also made time-variable, depending on the output error, the speed of convergence can be significantly improved.

A new variable length LMS algorithm: theoretical analysis and implementations

This paper addresses the problem of finding the optimum length for the adaptive least mean square (LMS) filter. In almost all papers published in this field, the length of the adaptive filter is maintained constant and the values of the coefficients are modified such that the output mean squared error (MSE) is minimized. There are some practical applications where we need to have information about the length of the optimum Wiener solution. As an example in system identification, one needs to have not only accurate approximation of the coefficient values but also the number of the coefficients of the unknown system. Here we provide the theoretical analysis of the LMS algorithm where the length mismatch between the adaptive filter and the unknown filter is taken into account. Based on this theoretical analysis a new variable length LMS algorithm is introduced.

Decompositional methods for stack filtering using Fibonacci p -codes

Abstract Stack filters form a wide class of nonlinear filters which has received a great deal of attention during recent years. In this paper new decompositional methods based on Fibonacci p-codes for computing the output for different stack filters are presented. The computational complexities of these methods are also studied and numerical examples illustrating the benefits of using different values of p for different situations.

Tuning the smoothness of the recursive median filter

The median filter is a special case of nonlinear filters used for smoothing signals. Since the output of the median filter is always one of the input samples, it is conceivable that certain signals could pass through the median filter unaltered. These signals define the signature of a filter and are referred to as root signals. Median filters are known to possess the convergence property, meaning that by repeating median filtering a root signal will be found, starting from any input signal. By associating the nonlinear operation of median filtering with a two terms cost function, an optimization process that minimizes that function is obtained. Cost functions of the same type are associated with different recursive median filtering schemes by replacing the actual values inside the filters window with the original signal. The convergence behavior of these filters and their smoothness are studied. By changing the positions of the replacements during filtering, a tuning effect of the smoothness is obtained. Simulation results show that the proposed filtering schemes provide improved performance over the standard recursive median filter, succeeding in preserving. small details and fine textures.

Adaptive denoising and lossy compression of images in transform domain

ing corthat uch 999 Abstract. A new algorithm for removing mixed noise from images based on combining an impulse removal operation with local adaptive filtering in transform domain is proposed in this paper. The key point is that the operation is designed so that it removes impulses while maintaining as much as possible of the frequency content of the original image. The second stage is an adaptive denoising operation based on local transform. The proposed algorithm works well in denoising images corrupted by a white (Gaussian, Laplacian, exponential) noise, impulsive noise, and their mixtures. Comparison of the new algorithm with known techniques for removing mixed noise from images shows the advantages of the new approach, both quantitatively and visually. In this paper we also apply transformbased denoising methods for removing blocking and ringing artifacts from decompressed block transform or wavelet coded images. The method is universal and applies to any compression method used.

A multiresolution method for singular points detection in fingerprint images

The detection of the singular points (cores and deltas) is the most important task of the fingerprint image classification operation. We propose a new method of detection and localization of singular points in a fingerprint image. Dealing with a multiresolution representation of both the orientation field and the certainty level, the proposed method achieves a quite high precision in the localization of the singular points. A 2/spl times/2 pixels block at a certain resolution level is classified as ordinary, core or delta region based on the existent relationships among the directions exhibited by its four pixels. An index equivalent with the Poincare index is proposed here to perform this classification.