Abstract. An Unsupervised Font clustering technique is proposed in this work. The new approach is based on global texture analysis, using high order statistic features, Gaussian classifier and a stochastic version of the EM algorithm. The font recognition is performed by taking the document as a simple image, where one or several types of fonts are present. The identification is not performed letter by letter as with conventional approaches. In the proposed method a window analysis is employed to obtain the features of the document, using fourth and third order moments. The new technique does not involve a study of local typography; therefore, it is content independent. A detailed study was performed with 8 types of fonts commonly used in the Spanish language. Each type of font can have four styles that lead, to 32 font combinations. The font recognition with clean images is 100% accurate.

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Multimodal biometric schemes arise as an interesting solution to the multidimensional reinforcement problem for biometric security systems. Along with the performance dimension, these systems should also comply with required levels for other conditions such as permanence, collectability, and circumvention, among others. In response to the demand for a multimodal and synchronous dataset, in this paper we introduce an open access database of synchronously recorded electroencephalogram signals (EEG), voice signals and video feed from 51 volunteers, 25 female, 26 male, captured for (but not limited to) biometric purposes. A total of 140 samples were collected from each user when pronouncing single digits in Spanish, giving a total of 7140 instances. EEG signals were captured using a 14-channel Emotiv ™ Epoc headset. The resulting set becomes a valuable resource when working on unimodal biometric systems, but significantly more for the evaluation of multimodal variants. Furthermore, the usefulness of the collected signals extends to being exploited by projects in brain computer interfaces and face recognition to name just a few. As an initial report on data separability of the related samples, six user recognition experiments are presented: a face recognition identifier with accuracy of 99%, two speaker identification systems with maximum accuracy of 100%, a bimodal face-speech verification case with Equal Error Rate around 2.64, an EEG identification example, and a bimodal user identification exercise based on EEG and voice modalities with a registered accuracy of 97.6%.

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