Josef Alois Birchbauer

Modelling fingerprint ridge orientation using Legendre polynomials

The estimation of fingerprint ridge orientation is an essential step in every automatic fingerprint verification system. The importance of ridge orientation can be deflected from the fact that it is inevitably used for detecting, describing and matching fingerprint features such as minutiae and singular points. In this paper we propose a novel method for fingerprint ridge orientation modelling using Legendre polynomials. One of the main problems it addresses is smoothing orientation data while preserving details in high curvature areas, especially singular points. We show that singular points, which result in a discontinuous orientation field, can be modelled by the zero-poles of Legendre polynomials. The models parameters are obtained in a two staged optimization procedure. Another advantage of the proposed method is a very compact representation of the orientation field, using only 56 coefficients. We have carried out extensive experiments using a state-of-the-art fingerprint matcher and a singular point detector. Moreover, we compared the proposed method with other state-of-the-art fingerprint orientation estimation algorithms. We can report significant improvements in both singular point detection and matching rates.

Multimodal highway monitoring for robust incident detection

We present detection and tracking methods for highway monitoring based on video and audio sensors, and the combination of these two modalities. We evaluate the performance of the different systems on realistic data sets that have been recorded on Austrian highways. It is shown that we can achieve a very good performance for video-based incident detection of wrong-way drivers, still standing vehicles, and traffic jams. Algorithms for simultaneous vehicle and driving direction detection using microphone arrays were evaluated and also showed a good performance on these tasks. Robust tracking in case of difficult weather conditions is achieved through multimodal sensor fusion of video and audio sensors.

Expert system segmentation of face images

Robust image analysis of photographs for personal documents has been an important open research problem for many years and the interest has been increased by introduction of electronic personal documents, which contain personal digital photographs. International Civil Aviation Organization (ICAO) has defined a set of recommendations defining minimal quality requirements that personal photographs stored in electronic personal documents must satisfy. Some image quality requirements apply only to certain image regions so exact position and location of the image regions has to be known in advance. In this paper, we propose a new knowledge-based method for segmentation of color personal photographs into five regions: skin, hair, shoulders, background, and padding frame. Prior to application of our method, the input image has to be normalized so that both eyes of a person are at the predefined positions within the image. To the best of our knowledge, no method for analysis of personal document photographs has been published in the literature that performs such segmentation. The proposed method consists of two main steps: (i) mean-shift segmentation step; and (ii) region labeling step based on a rule-based expert system. The most important component of the system is a set of rules specifically developed to enable robust labeling of personal document image regions. Extensive experimental validation has been conducted on four image sets and has demonstrated the accuracy and robustness of the proposed method.

Active Fingerprint Ridge Orientation Models

This paper proposes a statistical model for fingerprint ridge orientations. The active fingerprint ridge orientation model (AFROM) iteratively deforms to fit the orientation field (OF) of a fingerprint. The OFs are constrained by the AFROM to vary only in ways according to a training set. The main application of the method is the OF estimation in noisy fingerprints as well as the interpolation and extrapolation of larger OF parts. Fingerprint OFs are represented by Legendre Polynomials. The method does not depend on any pre-alignment or registration of the input image itself. The training can be done fully automatic without any user interaction. We show that the model is able to extract the significant appearance elements of fingerprint flow patterns even from noisy training images. Furthermore, our method does not depend on any other computed data, except a segmentation. We evaluated both, the generalisation as well as the prediction capability of the proposed method. These evaluations assess our method very good results.

Next-generation 3D visualization for visual surveillance

Existing visual surveillance systems typically require that human operators observe video streams from different cameras, which becomes infeasible if the number of observed cameras is ever increasing. In this paper, we present a new surveillance system that combines automatic video analysis (i.e., single person tracking and crowd analysis) and interactive visualization. Our novel visualization takes advantage of a high resolution display and given 3D information to focus the operators attention to interesting/ critical areas of the observed area. This is realized by embedding the results of automatic scene analysis techniques into the visualization. By providing different visualization modes, the user can easily switch between the different modes and can select the mode which provides most information. The system is demonstrated for a real setup on a university campus.

A hybrid fingerprint matcher in memory constrained environments

Hybrid fingerprint matchers are well known as a powerful tool for high security applications where the reliability of a single fingerprint characteristic is not high enough for the intended application. In this paper, we propose a novel method, which due to its compressibility can be applied in memory constrained environments. This is important for application in smart cards and independent identification modules, which recently gained popularity. The proposed method uses minutia point matcher as the first stage of matching, and, after successfully completing this stage, the second stage of matching is based on comparing the homogeneity of a direction map. The direction map is compressed using a quad tree.

Memory efficient fingerprint verification

Fingerprint recognition and verification are often based on local fingerprint features, usually ridge endings or terminations, also called minutiae. By exploiting the structural uniqueness of the image region around a minutia, the fingerprint recognition performance can be significantly enhanced. However, for most fingerprint images the number of minutia image regions (MIRs) becomes dramatically large, which imposes - especially for embedded systems - an enormous memory requirement. Therefore, we are investigating different algorithms for compression of minutia regions. The requirement for these algorithms is to achieve a high compression rate (about 20) with minimum loss in the matching performance of minutia image region matching. We investigate the matching performance for compression algorithms based on the principal component and the wavelet transformation. The matching results are presented in form of normalized ROC curves and interpreted in terms of compression rates and the MIR dimension.

Multi-cue learning and visualization of unusual events

Unusual event detection, i.e., identifying unspecified rare/critical events, has become one of the major challenges in visual surveillance. The main solution for this problem is to describe local or global normalness and to report events that do not fit to the estimated models. The majority of existing approaches, however, is limited to a single description (e.g., either appearance or motion) and/or builds on inflexible (unsupervised) learning techniques, both clearly degrading the practical applicability. To overcome these limitations, we demonstrate a system that is capable of extracting and modeling several representations in parallel, while in addition allows for user interaction within a continuous learning setup. Novel yet intuitive concepts of result visualization and user interaction will be presented that allow for exploiting the underlying data.

A pedestrian detection system combining motion detection, spatial grouping and classification

Motion is a strong cue for the pedestrian detection task. Several motion detection approaches exist which segment moving foreground regions quite reliably, nevertheless, correct estimation of a class label for the segmented objects still represents a challenge. Certain object classes such as pedestrian groups and vehicles are difficult to discriminate from each other based on the geometric properties of foreground segments only. While appearance-based detection approaches enable class-specific detectors, pose and view-point variations, small-sized objects adversely affect the detection performance. In this paper we combine a (i) motion-based detector - having the generic ability of detecting and outlining arbitrary moving objects-, and (ii) an appearance-based detector exhibiting class specificity. Scale-adaptive mean shift clustering is used to delineate regions of moving foreground. Within the delineated clusters the appearance-based pedestrian detector is used to estimate the label of the object class. For the pedestrian class, such as a group of pedestrians, a simple model-based verification is used to estimate the location of humans. The proposed use of spatial context shows to improve the performance of the overall pedestrian detection system in terms of significantly lower false alarm rates while maintaining about the same detection rate, as evaluated in the paper quantitatively. Real-time performance is achieved.

Classifier fusion for robust ICAO compliant face analysis

Biometrics is a huge and very fast growing domain of methods for uniquely recognizing humans based on one or more intrinsic physical or behavioral traits with applications in many different areas, e.g., surveillance, person verification and identification. The International Civil Aviation Organization (ICAO) provides a number of specifications to prepare automated recognition from travel document photos. The goal of these specifications is to increase security in civil aviation on the basis of standardized biometric data. Due to this international standard, there is a high demand for automatically checking face images to assist civil service employees in decision-making. In this work, we present a face normalization and analysis system implementing several parts of the ICAO specification. Our key contribution of this analysis is the fusion of different established classifiers to boost performance of the overall system. Our results show the superior checking quality on facial images due to utilizing classifier fusion compared to a single classifier decision.