Kenneth Nilsson

Localization of corresponding points in fingerprints by complex filtering

For the alignment of two fingerprints certain landmark points are needed. These should be automaticaly extracted with low misidentification rate. As landmarks we suggest the prominent symmetry points (singular points, SPs) in the fingerprints. We identify an SP by its symmetry properties. SPs are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. Complex filters, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.

Recognition by symmetry derivatives and the generalized structure tensor

We suggest a set of complex differential operators that can be used to produce and filter dense orientation (tensor) fields for feature extraction, matching, and pattern recognition. We present results on the invariance properties of these operators, that we call symmetry derivatives. These show that, in contrast to ordinary derivatives, all orders of symmetry derivatives of Gaussians yield a remarkable invariance: they are obtained by replacing the original differential polynomial with the same polynomial, but using ordinary coordinates x and y corresponding to partial derivatives. Moreover, the symmetry derivatives of Gaussians are closed under the convolution operator and they are invariant to the Fourier transform. The equivalent of the structure tensor, representing and extracting orientations of curve patterns, had previously been shown to hold in harmonic coordinates in a nearly identical manner. As a result, positions, orientations, and certainties of intricate patterns, e.g., spirals, crosses, parabolic shapes, can be modeled by use of symmetry derivatives of Gaussians with greater analytical precision as well as computational efficiency. Since Gaussians and their derivatives are utilized extensively in image processing, the revealed properties have practical consequences for local orientation based feature extraction. The usefulness of these results is demonstrated by two applications: 1) tracking cross markers in long image sequences from vehicle crash tests and 2) alignment of noisy fingerprints.

Complex Filters Applied to Fingerprint Images Detecting Prominent Symmetry Points Used for Alignment

For the alignment of two fingerprints position of certain landmarks are needed. These should be automatically extracted with low misidentification rate. As landmarks we suggest the prominent symmetry points (core-points) in the fingerprint. They are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. Complex filters, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.

Orientation scanning to improve lossless compression of fingerprint images

While standard compression methods available include complex source encoding schemes, the scanning of the image is often performed by a horizontal (row-by-row) or vertical scanning. In this work a new scanning method, called ridge scanning, for lossless compression of fingerprint images is presented. By using ridge scanning our goal is to increase the redundancy in data and thereby increase the compression rate. By using orientations, estimated from the linear symmetry property of local neighbourhoods in the fingerprint, a scanning algorithm which follows the ridges and valleys is developed. The properties of linear symmetry are also used for a segmentation of the fingerprint into two parts, one part which lacks orientation and one that has it. We demonstrate that ridge scanning increases the compression ratio for Lempel-Ziv coding as well as recursive Huffman coding with approximately 3% in average. Compared to JPEG-LS, using ridge scanning and recursive Huffman the gain is 10% in average.

Prominent symmetry points as landmarks in fingerprint images for alignment

For the alignment of two fingerprints the position of certain landmarks are needed. These should be automatically extracted with a low misidentification rate. As landmarks we suggest the prominent symmetry points (core-points) in the fingerprint. They are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. Complex filters, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.

A fiber-optic interconnection concept for scaleable massively parallel computing

One of the most important features of interconnection networks for massively parallel computer systems is scaleability. The fiber-optic network described in this paper uses both wavelength division multiplexing and a configurable ratio between optics and electronics to gain an architecture with good scaleability. The network connects distributed modules together to a huge parallel system where each node itself typically consists of parallel processing elements. The paper describes two different implementations of the star topology, one uses an electronic star and fiber optic connections, the other is purely optical with a passive optical star in the center. The medium access control of the communication concept is presented and some scaleability properties are discussed involving also a multiple-star topology.

Using Linear Symmetry Features as a Pre-processing Step for Fingerprint Images

This paper presents the idea to use linear symmetry properties as a feature based pre-processing step for fingerprint images. These features contain structural information of the local patterns. The linear symmetry can be computed by using separable spatial filtering and therefore has the potential to be a fast pre-processing step. Our results indicate that minutiae can be located as well as can be assigned a certain class type. The type of minutiae matching in combination with geometrical matching increases the matching efficiency as compared to the pure geometrical matching.

Biometric Identification of Mice

We present a new application area for biometric recognition: the identification of laboratory animals to replace todays invasive methods. Through biometric identification a non invasive identification technique is applied with a code space that is restricted only by the uniqueness of the biometric identifier in use, and with an error rate that is predictable. In this work we present the blood vessel pattern in a mouse-ear as a suitable biometric identifier used for mouse identification. Genuine and impostor score distributions are presented using a total of 50 mice. An EER of 2.5% is reported for images captured at the same instance of time which verifies the distinctive property of the biometric identifier

A modular, massively parallel computer architecture for trainable real-time control systems

Abstract A new system-architectural concept for trainable real-time control systems is based on resource adequacy both in processing and communication. Cyclically executing programs in distributed nodes communicate via a shared high-speed medium. Static scheduling of programs and communication implies that the maximum possible work-load can always be handled in a time-deterministic manner. The use of Artificial Neural Networks (ANN) algorithms and trainability implies a new system development strategy based on a Continuous Development paradigm. An implementation of the Architectural concept is presented. The communication speed is measured in Gbps and the access method is TDMA. An implementation of the system-development strategy is also presented.

Registration of fingerprints by complex filtering and by 1d projections of orientation images

When selecting a registration method for fingerprints, the choice is often between a minutiae based or an orientation field based registration method. In selecting a combination of both methods, instead of selecting one of the methods, we obtain a one modality multi-expert registration system. If the combined methods are based on different features in the fingerprint, e.g. the minutiae points respective the orientation field, they are uncorrelated and a higher registration performance can be expected compared to when only one of the methods are used. In this paper two registration methods are discussed that do not use minutiae points, and are therefore candidates to be combined with a minutiae based registration method to build a multi-expert registration system for fingerprints with expected high registration performance. Both methods use complex orientations fields but produce uncorrelated results by construction. One method uses the position and geometric orientation of symmetry points, i.e. the singular points (SPs) in the fingerprint to estimate the translation respectively the rotation parameter in the Euclidean transformation. The second method uses 1D projections of orientation images to find the transformation parameters. Experimental results are reported.