Individual identification of laboratory rodents typically involves invasive methods, such as tattoos, ear clips, and implanted transponders. Beyond the ethical dilemmas they may present, these methods may cause pain or distress that confounds research results. The authors describe a prototype device for biometric identification of laboratory rodents that would allow researchers to identify rodents without the complications of other methods. The device, which uses the rodent's ear blood vessel pattern as the identifier, is fast, automatic, noninvasive, and painless.

We present a new application area for biometric recognition: the identification of laboratory animals to replace today's 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.

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 di#erent 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.

A common framework for feature extraction in fingerprints is proposed by use of certain symmetries. The proposal includes representation, filters, and filtering techniques for common features including minutiae points, singular points and the ridge and valley patterns.

The filters are complex and are designed to identify certain symmetries called rotational symmetries and they are applied to the squared complex gradient field of an image. The filters are used as extractors for known fingerprint features. The filter response magnitude is a certainty measure for existence of a symmetry and its argument is the spatial orientation of that symmetry. This means that the position and the spatial orientation of the fingerprint feature are estimated in a single filtering step jointly. In the proposed framework the position and orientation of singular points are extracted using a multi-scale filtering technique. This strategy is taken to increase the signal-to-noise ratio in the extraction and can be done because singular points have a large spatial support from the orientation field. Experiments show that position is extracted by a precision of 5 ± 3 pixels1 and the orientation by a precision of 0 ± 4° with an EER of approximately 4%. The estimated position and orientation of singular points are used in an alignment experiment which yielded an unbiased alignment error with a standard deviation of 13 pixels 1.

A one modality multi-expert registration experiment is presented using singular points and orientation images to estimate the registration parameters.

1A fingerprint wavelength is in average 10 pixels.

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.

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.

We suggest a set of complex differential operators, symmetry derivatives, that can be used for matching and pattern recognition. We present results on the invariance properties of these. These show that 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 scalars. Moreover, these functions are closed under convolution and they are invariant to the Fourier transform. The revealed properties have practical consequences for local orientation based feature extraction. This is shown by two applications: i) tracking markers in vehicle tests ii) alignment of fingerprints.

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.

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.

For the alignment of two fing erprints 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 fing erprint. 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 filter s, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.