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The Devices for Assisted Living(DALi ) project is a research initiative sponsored by the European Commission under the FP7 programme aiming for the development of a robotic device to assist people with cognitive impairments in navigating complex environments. The project revisits the popular paradigm of the walker enriching it with sensing abilities (to perceive the environment), with cognitive abilities (to decide the best path across the space) and with mechanical, visual, acoustic and haptic guidance devices (to guide the person along the path). In this paper, we offer an overview of the developed system and describe in detail some of its most important technological aspects

Smoothing fingerprint ridge orientation involves a principal discrepancy. Too little smoothing can result in noisy orientation fields (OF), too much smoothing will harm high curvature areas, especially singular points (SP). In this paper we present a fingerprint ridge orientation model based on Legendre polynomials. The motivation for the proposed method can be found by analysing the almost exclusively used method in literature for orientation smoothing, proposed by Witkin and Kass [5] more than two decades ago. The main contribution of this paper argues that the vectorial data (sine and cosine data) should be smoothed in a coupled way and the approximation error should not be evaluated employing vectorial data. For evaluating the proposed method we use a Poincáre-Index based SP detection algorithm. The experiments show, that in comparison to competing methods the proposed method has improved orientation smoothing capabilities, especially in high curvature areas.

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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 (MIR's) 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. In this paper 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.