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This paper proposes the application of measures based on nonlinear dynamics for emotional speech characterization. Measures such as mutual information, dimension correlation, entropy correlation, Shannon’s entropy, Lempel–Ziv complexity and Hurst exponent are extracted from the samples of a database of emotional speech. Then, summary statistics such as mean, standard deviation, skewness and kurtosis are applied on the extracted measures. Experiments were conducted on the Berlin emotional speech database for a three-class problem (neutral, fear and anger as emotional states). Feature selection is accomplished and a methodology is proposed to find the best features. In order to evaluate the discrimination ability of the selected features, a neural network classifier is used. The global success rate is 93.78 ± 3.18 %.

Juan Jose Fuertes Cebrian1, Carlos Manuel Travieso Gonzalez2 and Valery Naranjo Ornedo1 1Instituto Interuniversitario de Investigacion en Bioingenieria y Tecnologia Orientada al Ser Humano, Universitat Politecnica de Valencia, I3BH/Labhuman 2Instituto Universitario para el Desarrollo Tecnologico y la Innovacion en las Comunicaciones (IDETIC), Departamento de Senales y Comunicaciones, Universidad de Las Palmas de Gran Canaria Spain

This work presents an improvement of the automatic and supervised spider identification approach based on biometric spider web analysis. We have used as feature extractor, a Joint Approximate Diagonalization of Eigen-matrixes Independent Component Analysis applying to a binary image with a reduced size (20×20 pixels) from the colour original image. Finally, we have applied a least square support vector machine as classifier, reaching over 98.15% in our hold-50%-out validation. This system is making easier Biologists’ tasks in this field, because they can have a second opinion or have a tool for this work.

Features representing information about pressure distribution from a static image of a handwritten signature are analyzed for an offline verification system. From gray-scale images, its histogram is calculated and used as "spectrum'' for calculation of pseudo-cepstral coefficients. Finally, the unique minimum-phase sequence is estimated and used as feature vector for signature verification. The optimal number of pseudo-coefficients is estimated for best system performance. Experiments were carried out using a database containing signatures from 100 individuals. The robustness of the analyzed system for simple forgeries is tested out with a LS-SVM model. For the sake of completeness, a comparison of the results obtained by the proposed approach with similar works published using pseudo-dynamic feature for offline signature verification is presented.

The verification of identity is becoming a crucial factor in our hugely interconnected society. Questions such as “Is she really who she claims to be?”, “Is this person authorized to use this facility?” are routinely being posed in a variety of scenarios ranging from issuing a driver’s license to gaining entry into a country. The necessity for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometric systems, described as the science in order to recognize an individual based on his or her physical or behavioural traits, is beginning to get acceptance as a legitimate method in order to determine an individual’s identity. Nowadays, biometric systems have been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In particular, this work presents a non-cooperative identification system based on facial biometric.

Today, advances in Computer Science and the proliferation of computers in modern society are an unquestionable fact. Nevertheless, the continuing importance of orthography and the hand-written document are also beyond doubt. The new technologies permit us to work with online information collecting, but there is still a large quantity of information in our society which requires using algorithms for samples off-line. Security in certain applications requires having biometric systems for their identification; in particular, banking checks, wills, postcards, invoices, medical prescriptions, etc, require the identity of the person who has written them to be verified. The only way to do this is with writer recognition techniques. Furthermore, many hand-written documents are vulnerable to possible forgeries, deformations or copies, and generally, to illicit misuse. Therefore, a high percentage of routine work is carried out by experts and professionals in this field, whose task is to certify and to judge the authenticity or falsehood of handwritten documents (for example: wills) in a judicial procedure. Therefore nowadays research on writer identification is an active field. At present, some software tools enable certain characteristics to be displayed and visualised by experts and professionals, but these experts need to devote a great deal of time to such investigations before they are able to draw up conclusions about a given body of writing. Therefore, these tools are not time-saving and nor do they provide a meticulous analysis of the writing. They have to work with graph paper and templates in order to obtain parameters (angles, dimensions of the line, directions, parallelisms, curvatures, alignments, etc.). Moreover, they have to use a magnifying glass and graph paper in order to measure angles and lines. This research aims to lighten this arduous task.