Miguel Angel Ferrer-Ballester

Authentication of Individuals using Hand Geometry Biometrics: A Neural Network Approach

Biometric based systems for individual authentication are increasingly becoming indispensable for protecting life and property. They provide ways for uniquely and reliably authenticating people, and are difficult to counterfeit. Biometric based authenticity systems are currently used in governmental, commercial and public sectors. However, these systems can be expensive to put in place and often impose physical constraint to the users. This paper introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system using neural networks. The proposed approach followed to construct this system consists of an acquisition device, a pre-processing stage, and a neural network based classifier. One of the novelties of this work comprises on the introduction of hand geometry’s related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Another novelty of this research comprises on the use of error correction codes to enhance the level of performance of the neural network model. A dataset made of scanned images of the right hand of fifty different people was created for this study. Identification rates and Detection Cost Function (DCF) values obtained with the system were evaluated. Several strategies for coding the outputs of the neural networks were studied. Experimental results show that, when using Error Correction Output Codes (ECOC), up to 100% identification rates and 0% DCF can be obtained. For comparison purposes, results are also given for the Support Vector Machine method.

Support vector machines applied to the detection of voice disorders

Support Vector Machines (SVMs) have become a popular tool for discriminative classification. An exciting area of recent application of SVMs is in speech processing. In this paper discriminatively trained SVMs have been introduced as a novel approach for the automatic detection of voice impairments. SVMs have a distinctly different modelling strategy in the detection of voice impairments problem, compared to other methods found in the literature (such a Gaussian Mixture or Hidden Markov Models): the SVM models the boundary between the classes instead of modelling the probability density of each class. In this paper it is shown that the scheme proposed fed with short-term cepstral and noise parameters can be applied for the detection of voice impairments with a good performance.

Efficient adaptive vector quantization of LPC parameters

This correspondence presents a new two-stage adaptive vector quantizer of LSF parameters in LPC speech coding. The first codebook is adapted by a partition-delete operation, whereas the code-vectors of the second codebook remain unchanged. The objective and subjective evaluations show that the proposed scheme offers transparent quantization with 22 b/frame. >

Thermal hand image segmentation for biometric recogniton

Image segmentation is a first step for most of the biometric systems. In this paper we present a hand image segmentation algorithm for thermographic images. This work is based on a specially acquired database with a thermographic camera TESTO 882-3, which acquires thermal images of 320×240 pixels as well as visible images of 640×480 pixels. Experimental results reveal that thermal images are harder to segment from the background than visible images. Specially for some users. However, thermal images provide some interesting information, such as the vein pattern distribution.