Miguel A. Ferrer

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.

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.

Robustness of Offline Signature Verification Based on Gray Level Features

Several papers have recently appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP8,1riu2 plus LBP16,2riu2 and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white “nondistorting” background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.

Automatic detection of pathologies in the voice by HOS based parameters

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 parameterize 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 98.3% has been obtained by using both the classic and the HOS based proposed parameters.

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.

Comparing infrared and visible illumination for contactless hand based biometric scheme

This paper presents two contact-free biometric identification system based on geometrical features of the human hand. The right hand images are acquired by a commercial modified Web cam with a 320times240 pixels resolution. The main difference between both systems is the illumination. A 60 W visible range bulb was the first choice. An infra-red light was used in the second approach to solve segmentation problems in real environments. The geometrical features are obtained from the binarized images and consist in normalized measures of the index, middle and ring finger for the infra-red system and projective invariants features for the visible light system. A support vector machines is used as verifier.

Fast Affine Projection Algorithms for Filtered-x Multichannel Active Noise Control

In recent years, affine projection algorithms have been proposed for adaptive system applications as an efficient alternative to the slow convergence speed of least mean square (LMS)-type algorithms. Whereas much attention has been focused on the development of efficient versions of affine projection algorithms for echo cancellation applications, the similar adaptive problem presented by active noise control (ANC) systems has not been studied so deeply. This paper is focused on the necessity to reduce even more the computational complexity of affine projection algorithms for real-time ANC applications. We present some alternative efficient versions of existing affine projection algorithms that do not significantly degrade performance in practice. Furthermore, while in the ANC context the commonly used affine projection algorithm is based on the modified filtered-x structure, an efficient affine projection algorithm based on the (nonmodified) conventional filtered-x structure, as well as efficient methods to reduce its computational burden, are discussed throughout this paper. Although the modified filtered-x scheme exhibits better convergence speed than the conventional filtered-x structure and allows recovery of all the signals needed in the affine projection algorithm for ANC, the conventional filtered-x scheme provides a significant computational saving, avoiding the additional filtering needed by the modified filtered-x structure. In this paper, it is shown that the proposed efficient versions of affine projection algorithms based on the conventional filtered-x structure show good performance, comparable to the performance exhibited by the efficient approaches of modified filtered-x affine projection algorithms, and also achieve meaningful computational savings. Experimental results are presented to validate the use of the algorithms introduced in the paper for practical applications.

Improved palmprint authentication using contactless imaging

Palmprint identification has emerged as one of the popular and promising biométrie modalities for forensic and commercial applications. In recent years the contactless system emerges as a viable option to address hygienic issues and improve the user acceptance. The presence of significant scale, rotation, occlusion and translation variations in the contactless palmprint images requires the feature extraction approaches which are tolerant to such changes. Therefore the usage of traditional palmprint feature extraction methods on contactless imaging schemes remains questionable and hence all/popular palmprint feature extraction methods may not be useful in contactless frameworks. This paper we systematically examine the issues related to the contactless palmprint authentication and presents performance evaluation on the two public databases. Our experimental results on more than 4300 images from two contactless databases suggests that the Scale Invariant Feature Transform (SIFT) features perform significantly better for the contactless palmprint images than the (most) promising Orthogonal Line Ordinal Features (OLOF) approach employed earlier on the more conventional palmprint imaging. Our experimental results further suggests that the combination of robust SIFT matching scores along with those from OLOF can be employed to achieve more reliable performance improvement. The achieved error rates show a good performance of these features in controlled and uncontrolled environments conditions with the error rates similar to other contact based approaches.