This paper presents a set of geometric signature features for offline automatic signature verification based on the description of the signature envelope and the interior stroke distribution in polar and Cartesian coordinates. The features have been calculated using 16 bits fixed-point arithmetic and tested with different classifiers, such as hidden Markov models, support vector machines, and Euclidean distance classifier. The experiments have shown promising results in the task of discriminating random and simple forgeries.

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In the current panorama the conclusive identification of a laryngeal pathology relies inevitably on the observation of the vocal folds by means of laryngoscopical techniques. This inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and, above all, the fact that it is an invasive technique. This paper looks into the possibility of measuring the quality of a voice starting from an audio recording. The existing parameters in current literature ("classic parameters") which allow quantifying the quality of a voice have been studied, and the parameters that present better results have been selected. Also, seven new High Order Statistics (HOS) based parameters are proposed to parametrize the voice signal. On the other hand, a software package has been developed which carries out the automatic detection of dysfunction in phonation. A success rate of % has been obtained by using both the classic and the HOS based proposed parameters.

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