Fusion de classifieurs en utilisant la théorie de l'évidence pour l'amélioration de la classification d'image

Le problème traité dans cet article concerne l'amélioration de la classification d'image dans les conditions d'insuffisance d'informations a priori déterministes et fiables sur l'état et la nature de la formation de l'image à l'instant de la prise de vue. Une méthode de fusion des classifieurs d'imagerie pour une meilleure classification des scènes imagées est proposée. La méthode est basée sur la théorie de l'évidence. Elle est générale et applicable à tout type de classifieur. On utilise le taux de fiabilité de la classification comme critère d'évaluation, les résultats obtenus, en utilisant des images simulées et réelles de télédétection, montrent que la méthode proposée donne de meilleurs résultats en comparaison avec les résultats des classifieurs considérés séparément

Approches neuronales pour l'extraction des composantes principales d'images multispectrales de télédétection

Le problème traité dans le présent article consiste en l'extraction des composantes principales les plus significatives d'images multispectrales de télédétection sans avoir à calculer la matrice de covariance des images spectrales. L'originalité du travail réside dans l'élaboration des algorithmes d'apprentissage spécifiques pour deux approches neuronales d'Analyse en Composantes Principales (ACP). Les deux approches possèdent des convergences rapides. L'application sur une image multispectrale réelle a montré leur efficacité dans l'extraction des composantes principales les plus significatives. The problem addressed in the présent paper is the most significant principal components extraction of remotely sensed multispectral images without having to calculate the covariance matrix of spectral images. The originality of the work resides in the elaboration of specific training algorithms for two neural network-based approaches of Principal Component Analysis (PCA). The convergence of a proposed approaches are rapid. The application on a real multispectral image has shown their efficiency in the extraction of the most significant principal components

Digital Implementation of an Improved LTE Stream Cipher Snow-3G Based on Hyperchaotic PRNG

SNOW-3G is a stream cipher used by the 3GPP standards as the core part of the confidentiality and integrity algorithms for UMTS and LTE networks. This paper proposes an enhancement of the regular SNOW-3G ciphering algorithm based on HC-PRNG. The proposed cipher scheme is based on hyperchaotic generator which is used as an additional layer to the SNOW-3G architecture to improve the randomness of its output keystream. The objective of this work is to achieve a high security strength of the regular SNOW-3G algorithm while maintaining its standardized properties. The originality of this new scheme is that it provides a good trade-off between good randomness properties, performance, and hardware resources. Numerical simulations, hardware digital implementation, and experimental results using Xilinx FPGA Virtex technology have demonstrated the feasibility and the efficiency of our secure solution while promising technique can be applied to secure the new generation mobile standards. Thorough analysis of statistical randomness is carried out demonstrating the improved statistical randomness properties of the new scheme compared to the standard SNOW-3G, while preserving its resistance against cryptanalytic attacks

An efficient palmprint identification system using multispectral and hyperspectral imaging

Ensuring the security of individuals is becoming an increasingly important problem in a variety of applications. Biometrics technology that relies on the physical and/or behavior human characteristics is capable of providing the necessary security over the standard forms of identification. Palmprint recognition is a relatively new one. Almost all the current palmprint- recognition systems are mainly based on image captured under visible light. However, multispectral and hyperspectral imaging have been recently used to improve the performance of palmprint identification. In this paper, the MultiSpectral Palmprint (MSP) and HyperSpectral Palmprint (HSP) are integrated in order to construct an efficient multimodal biometric system. The observation vector is based on Principal Components Analysis (PCA). Subsequently, HiddenMarkov Model (HMM) is used for modeling this vector. The proposed scheme is tested and evaluated using 350 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate

An efficient multi-spectral palmprint identification using contourlet decomposition and Hidden Markov Model

Automatic personal identification is playing an important role in security systems. Biometrics technologies has been emerging as a new and effective methods to achieve accurate and reliable identification results. A number of biometric traits exist and are in use in various applications. Palmprint is one of the relatively new biometrics due to its stable and unique characteristics. In this paper, multi-spectral information for the unique palmprint are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. For that, the palm lines are characterized by the contourlet coefficients sub-bands and compressed using the Principal Components Analysis (PCA). Subsequently, we use the Hidden Markov Model (HMM) for modeling. Finally, log-likelihood scores are used for palmprint matching. Experimental results show that our proposed scheme yields the best performance for identifying palmprints and it is able to provide an excellent identification rate and provide more security

Efficient person identification by fusion of multiple palmprint representations

The automatic person identification is a significant component in any security biometric system because of the challenges and the significant number of the applications that require a high safety. A biometric system based solely on one template (representation) is often not able to meet such desired performance requirements. Identification based on multiple representations represents a promising tendency. In this context, we propose here a multi-representation biometric system for person recognition using palm images and by integrating two different representations of the palmprint. Two ensembles of matchers that use two different feature representation schemes of the images are considered. The two different feature extraction methods are the block based 2D Discrete Cosine Transform (2D-DCT) and the phase information in 2D Discrete Fourier Transform (2D-DFT) that are complementing each other in terms of identification accuracy. Finally the two ensembles are combined and the fusion is applied at the matching-score level. Using the PolyU palmprint database, The results showed the effectiveness of the proposed multi-representation biometric system in terms of the recognition rate

Robust multispectral palmprint identification system by jointly using Contourlet decomposition & Gabor filter response

In current society, reliable identification and verification of individuals are becoming more and more necessary tasks for many fields, not only in police environment, but also in civilian applications, such as access control or financial transactions. Biometric systems are used nowadays in these fields, offering greater convenience and several advantages over traditional security methods based on something that you know (password) or something that you have (keys). In this paper, we propose an efficient online personal identification system based on Multi-Spectral Palmprint (MSP) images using Contourlet Transform (CT) and Gabor Filter (GF) response. In this study, the spectrum image is characterized by the contourlet coefficients sub-bands. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same spectrum is filtered by the Gabor filter. The real and imaginary responses of the filtering image are used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. Our experimental results show the effectiveness and reliability of the proposed method, which brings both high identification and accuracy rate

Are infrared images reliable for palmprint based personal identification systems?

Several studies for palmprint-based person identification have focused on the use of palmprint images captured in the visible part of the spectrum. However, to a possible improvement of the existing palmprint systems, the proposed work concerned with the use of infrared palmprint images for the palmprint identification system. For that, a comparison of infrared palmprint images versus gray level and color image is given. At the features-extraction stage the features are generated by the method of Principal Component Analysis (PCA). This feature-extraction technique has been widely used for pattern recognition, as well as in the field of biometrics. The proposed scheme is tested and evaluated using PolyU multispectral palmprint database of 400 users. Our experimental results show that the infrared spectrum achieves the best result. Also, color image present three spectrums, for that, we propose a score level and image level fusion schemes to integrate these colors information

Fusion of multispectral palmprint images for automatic person identification

has gained much attention in this subject recently. Many types of personal identification systems have been developed, and palmprint identification is one of the emerging technologies. This paper presents a novel biometric technique to automatic personal identification system using multispectral palmprint technology. In this method, each of spectrum images are aligned and then used to extract palmprint features using 1D log-Gabor filter. These features are then examined for their individual and combined performances. Finally, the hamming distance is used for matching of palmprint features. The experimental results showed that the proposed method achieve an excellent identification rate and provide more security