Martin Aastrup Olsen

Bio: Martin Aastrup Olsen is an academic researcher from Gjøvik University College. The author has contributed to research in topics: Biometrics & Fingerprint recognition. The author has an hindex of 12, co-authored 35 publications receiving 453 citations. Previous affiliations of Martin Aastrup Olsen include Technische Universität Darmstadt & Technical University of Denmark.

Finger image quality assessment features – definitions and evaluation

Abstract: Finger image quality assessment is a crucial part of any system where a high biometric performance and user satisfaction is desired. Several algorithms measuring selected aspects of finger image quality have been proposed in the literature, yet only few of them have found their way into quality assessment algorithms used in practice. The authors provide comprehensive algorithm descriptions and make available implementations of adaptations of ten quality assessment algorithms from the literature which operates at the local or the global image level. They evaluate the performance on four datasets in terms of the capability in determining samples causing false non-matches and by their Spearman correlation with sample utility. The authors' evaluation shows that both the capability in rejecting samples causing false non-matches and the correlation between features varies depending on the dataset.

Comprehensive analysis of spectral minutiae for vein pattern recognition

Abstract: Similar to biometric fingerprint recognition, characteristic minutiae points - here end and branch points - can be extracted from skeletonised vein images to distinguish individuals. An approach to extract those vein minutiae and to transform them into a fixed-length, translation and scale invariant representation where rotations can be easily compensated is presented in this study. The proposed solution based on spectral minutiae is evaluated against other comparison strategies on three different datasets of wrist and palm dorsal vein samples. The authors- analysis shows a competitive biometric performance while producing features that are compatible with state-of-the-art template protection systems. In addition, a modified and more distinctive, but not transform or rotation invariant, representation is proposed and evaluated.

Gabor filters as candidate quality measure for NFIQ 2.0

Abstract: Quality assessment of biometric fingerprint images is necessary to ensure high biometric performance in biometric recognition systems. We relate the quality of a fingerprint sample to the biometric performance to ensure an objective and performance oriented benchmark. The proposed quality metric is based on Gabor filter responses and is evaluated against eight contemporary quality estimation methods on four datasets using sample utility derived from the separation of genuine and imposter distributions as benchmark. The proposed metric shows performance and consistency approaching that of the composite NFIQ quality assessment algorithm and is thus a candidate for inclusion in a feature vector introducing the NFIQ 2.0 metric.

Improving Uncertainty Estimation in Convolutional Neural Networks Using Inter-rater Agreement

Abstract: Modern neural networks are pushing the boundaries of medical image classification. For some tasks in dermatology, state of the art models are able to beat human experts in terms of accuracy and type I/II errors. However, in the domain of medical applications, models should also be evaluated on how well they capture uncertainty in samples and labels. This aspect is key to building trust in computer-assisted systems, otherwise largely considered to be black boxes by their users. A common practice in supervised learning is to collect multiple evaluations per sample, which is particularly useful when inter-rater agreement is expected to be low. At the same time, model training traditionally uses label fusion, such as majority voting, to produce a single label for each sample. In this paper, we experimentally show that models trained to predict skin conditions become overconfident when this approach is used; i.e. the probability estimates of the model exceeds the true correctness likelihood. Additionally, we show that a better calibrated model is obtained when training with a label sampling scheme that takes advantage of inter-rater variability during training. The calibration improvements come at no cost in terms of model accuracy. Our approach is combined and contrasted with other recent techniques in uncertainty estimation. All experiments are evaluated on a proprietary dataset consisting of 31017 images of skin, where up to 12 experts have diagnosed each image.

Spectral minutiae for vein pattern recognition

Abstract: Similar to biometric fingerprint recognition, characteristic minutiae points - here end- and branch points - can be extracted from skeletonized veins to distinguish individuals. An approach to extract those vein minutiae and to transform them into a fixed-length, translation and scale invariant representation where rotations can be easily compensated is presented in this paper. The proposed solution based on spectral minutiae is evaluated against other comparison strategies on three different datasets of wrist and palm vein samples. It shows a competitive biometric performance while producing features that are compatible with state-of-the-art template protection systems.

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The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Biometric quality: a review of fingerprint, iris, and face

Abstract: Biometric systems encounter variability in data that influence capture, treatment, and u-sage of a biometric sample. It is imperative to first analyze the data and incorporate this understanding within the recognition system, making assessment of biometric quality an important aspect of biometrics. Though several interpretations and definitions of quality exist, sometimes of a conflicting nature, a holistic definition of quality is indistinct. This paper presents a survey of different concepts and interpretations of biometric quality so that a clear picture of the current state and future directions can be presented. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in fingerprint, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images, to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems. The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.

Method of applying a combined or hybrid sound-field control strategy

Abstract: A method of applying a combined control strategy for the reproduction of multichannel audio signals in two or more sound zones, the method comprising deriving a first cost function for controlling the acoustic potential energy, such as on the basis of the Acoustic Contrast Control method and/or the Energy Difference Maximation method, in the zones to obtain acoustic separation between the zones in terms of sound pressure, deriving a second cost function, such as the Pressure Matching method, controlling the phase of the sound provided in the zones, and where a weight is obtained for determining a combination of the first and second cost functions in a combined optimization.