Carlos M. Travieso

Offline geometric parameters for automatic signature verification using fixed-point arithmetic

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.

Off-line signature verification based on grey level information using texture features

A method for conducting off-line handwritten signature verification is described. It works at the global image level and measures the grey level variations in the image using statistical texture features. The co-occurrence matrix and local binary pattern are analysed and used as features. This method begins with a proposed background removal. A histogram is also processed to reduce the influence of different writing ink pens used by signers. Genuine samples and random forgeries have been used to train an SVM model and random and skilled forgeries have been used for testing it. Results are reasonable according to the state-of-the-art and approaches that use the same two databases: MCYT-75 and GPDS-100 Corpuses. The combination of the proposed features and those proposed by other authors, based on geometric information, also promises improvements in performance.

Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics

In this paper, we propose to quantify the quality of the recorded voice through objective nonlinear measures. Quantification of speech signal quality has been traditionally carried out with linear techniques since the classical model of voice production is a linear approximation. Nevertheless, nonlinear behaviors in the voice production process have been shown. This paper studies the usefulness of six nonlinear chaotic measures based on nonlinear dynamics theory in the discrimination between two levels of voice quality: healthy and pathological. The studied measures are first- and second-order Renyi entropies, the correlation entropy and the correlation dimension. These measures were obtained from the speech signal in the phase-space domain. The values of the first minimum of mutual information function and Shannon entropy were also studied. Two databases were used to assess the usefulness of the measures: a multiquality database composed of four levels of voice quality (healthy voice and three levels of pathological voice); and a commercial database (MEEI Voice Disorders) composed of two levels of voice quality (healthy and pathological voices). A classifier based on standard neural networks was implemented in order to evaluate the measures proposed. Global success rates of 82.47% (multiquality database) and 99.69% (commercial database) were obtained.

Off-line Handwritten Signature GPDS-960 Corpus

The current need for large databases to evaluate automatic biometric recognition systems has motivated the developing of the GPDS-960 corpus, an off-line handwritten signature database which contains 24 genuine signatures and 30 forgeries of 960 individuals. This paper describes the GPDS signature corpus, gives details about the acquisition protocols and presents preliminary verification results obtained using the GPDS data.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals

The objective of this paper is to provide a review of recent advances in automatic vibration- and audio-based fault diagnosis in machinery using condition monitoring strategies. It presents the most valuable techniques and results in this field and highlights the most profitable directions of research to present. Automatic fault diagnosis systems provide greater security in surveillance of strategic infrastructures, such as electrical substations and industrial scenarios, reduce downtime of machines, decrease maintenance costs, and avoid accidents which may have devastating consequences. Automatic fault diagnosis systems include signal acquisition, signal processing, decision support, and fault diagnosis. The paper includes a comprehensive bibliography of more than 100 selected references which can be used by researchers working in this field.

Low Cost Multimodal Biometric identification System Based on Hand Geometry, Palm and Finger Print Texture

This paper presents a multimodal biometric identification system based on the combination of geometrical, palm and finger print features of the human hand. The right hand images are acquired by a commercial scanner with a 150 dpi resolution. The geometrical features are obtained from the binarized images and consist on 15 measures. A support vector machines is used as verifier. The palm print and finger texture are obtained by means of different 20 Gabor phase encoding schemes. A robust coordinate system is defined to assure the image alignment. A Hamming distance and threshold are used for verifying the identity. A feature, score and decision level fusion results have shown the improvement of the combined scheme.

Biometric identification system by lip shape

Biometrics systems based on lip shape recognition are of great interest, but have received little attention in the scientific literature. This is perhaps due to the belief that they have little discriminative power. However, a careful study shows that the difference between lip outlines is greater than that between shapes at different lip images of the same person. So, biometric identification by lip outline is possible. In this paper the lip outline is obtained from a color face picture: the color image is transformed to the gray scale using the transformation of Chang et al. (1994) and binarized with the Ridler and Calvar threshold. Considering the lip centroid as the origin of coordinates, each pixel lip envelope is parameterized with polar (ordered from -/spl pi/ to +/spl pi/) and Cartesian coordinates (ordered as heights and widths). To asses identity, a multilabeled multiparameter hidden Markov model is used with the polar coordinates and a multilayer neural network is applied to Cartesian coordinates. With a database of 50 people an average classification hit ratio of 96.9% and equal error ratio (EER) of 0.015 are obtained.

Application of the Teager-Kaiser energy operator in bearing fault diagnosis

Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager-Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal.

On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia; it has a high socioeconomic impact in Western countries. Therefore, it is one of the most active research areas today. Alzheimer’s disease is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a postmortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early AD detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of AD by noninvasive methods. The purpose is to examine, in a pilot study, the potential of applying machine learning algorithms to speech features obtained from suspected Alzheimer’s disease sufferers in order to help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: spontaneous speech and emotional response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of AD patients.