Régis Fournier

A Postural Information-Based Biometric Authentication System Employing S-Transform, Radial Basis Function Network, and Extended Kalman Filtering

This paper proposes a new system for biometry-based human authentication, where postural signal information is utilized to identify a person. The system employs a novel approach where four types of temporal postural signals are acquired for each person to develop an authentication database, and for each posture, both signals in the - and -directions are utilized for the purpose of authentication. The proposed system utilizes S-transform, which is a joint time-frequency representation tool, to determine the characteristic features for each human posture. Based on these characteristic features, a radial basis function network (RBFN) system is developed for the purpose of specific authentication. The RBFN authentication system is developed by training it to employ extended Kalman filtering (EKF). The EKF-trained RBFN authentication system could produce overall authentication accuracy on the order of 94%-95% and could outperform similar authentication systems developed, which employ two very popular variants of backpropagation neural networks (BPNNs) and a variant of radial basis neural network (RBNN).

Fast communication: Generalized multi-directional discrete Radon transform

This paper presents a discrete generalized multi-directional Radon transform (GMDRT) and its exact inversion algorithm. GMDRT is an extension of the classical Radon transform. It aims to project parameterized curves and geometric objects following several directions. For this purpose, we propose an algebraic formalism of the Radon Transform presenting the forward transform as a matrix-vector_ multiplication. We show in this paper that the exact inversion of the GMDRT exists. This property allows useful applications, in the field of digital image processing.

Effect analysis of age and gender on postural stability using PCA decomposition

This paper presents an analysis of stabilogram using the Principal Component Analysis (PCA) decomposition. It shows also the effects of different aspects on the human postural stability. The stabilogram measures either lateral displacement or forward-backward displacement of a subject. These measurements are taken to quantify posture while standing in one of four controlled positions. By using Principal Component Analysis (PCA), the stabilogram is decomposed into three components with biological meaning. The components are trend, rambling and trembling. This paper proposes to create analytic signals for rambling (deterministic) and trembling (random) and use the resulting complex trajectories to identify the effect of age and gender on postural stability. The proposed method employs a signal analysis front end (PCA analysis) and a signal interpretation backend (clustering of complex trajectories). Experimental results show the efficiency of the PCA analysis to identify the effect of age and gender on the postural stability. These results are able to discriminate between control and young groups and indicate a less well-controlled posture for control subjects (34.5 ± 7.5yrs) relatively to young subjects (22.5 ± 2.5yrs). Results are also able to discriminate between female subjects and male subjects and indicate a less well-controlled posture for female subjects relatively to males.

A novel multimodal compression scheme based on a spiral insertion function in the wavelet domain

This paper presents a new variant of multimodal compression scheme based on a spiral insertion function in the wavelet domain. The main idea consists of inserting a decomposed signal into a decomposed image prior to a joint SPIHT compression process. The insertion phase is achieved in the detail sub-bands of the wavelet decomposition. The evaluation is assessed on both natural and medical images using objective and subjective comparison criteria. The experimental results showed the effectiveness of the proposed approach to obtain significant gains in terms of reconstructed signal (PRD %) and image (PSNR).

The Recognition of Polynomial Position and Orientation through the Finite Polynomial Discrete Radon Transform

In this paper, we propose to accurately detect from an image curvilinear features that can be approximated by polynomial curves. Having the a priori knowledge of a polynomial parameters (coefficients and degree), we give the possibility to recognize both the orientation and the position of the polynomial (if it exists) in the given image. For this objective, we present a new approach titled ”The Finite Polynomial Discrete Radon Transform” (FPDRT) that maps the initial image into a Radon space where each point presents the amount of evidence of the existence of a polynomial at the same position. The FPDRT sums the pixels centered on a polynomial and stores the result at the corresponding position in the Radon space. The FPDRT extends the formalism of the Finite Discrete Radon Transform(FRT) which is restricted to project the image along straight lines of equation y = mx + t where m and t are integers. Our method generalizes FRT by projecting the image with respect to polynomials of equation y = mx n + t where m, n and t are integers. The FPDRT method is exactly invertible, requires only arithmetic operations and is applicable to p×p sized images where p is a prime number. Several applications are allowable by the FPDRT such as fingerprint, palm print biometric applications and multi directional roads recognition.

The polynomial discrete Radon transform

This paper presents a new approach called polynomial discrete Radon transform (PDRT), regarded as a generalization of the classical finite discrete Radon transform. Specifically, the PDRT transforms an image into Radon space by summing the pixels according to polynomial curves. The PDRT can be applied on square

Supervised Biometric System Using Multimodal Compression Scheme

p \times p

Signal and Image Processing for Biometrics: Naït-Ali/Signal and Image Processing for Biometrics

p×p images where