Mohammed El Hassouni

Local appearance based face recognition method using block based steerable pyramid transform

In this paper, an efficient local appearance feature extraction method based on Steerable Pyramid (S-P) wavelet transform is proposed for face recognition. Local information is extracted by computing the statistics of each sub-block obtained by dividing S-P sub-bands. The obtained local features of each sub-band are combined at the feature and decision level to enhance face recognition performance. The purpose of this paper is to explore the usefulness of S-P as feature extraction method for face recognition. The proposed approach is compared with some related feature extraction methods such as principal component analysis (PCA), as well as linear discriminant analysis LDA and boosted LDA. Different multi-resolution transforms, wavelet (DWT), gabor, curvelet and contourlet, are also compared against the block-based S-P method. Experimental results on ORL, Yale, Essex and FERET face databases convince us that the proposed method provides a better representation of the class information, and obtains much higher recognition accuracies in real-world situations including changes in pose, expression and illumination.

HOS-based image sequence noise removal

In this paper, a new spatiotemporal filtering scheme is described for noise reduction in video sequences. For this purpose, the scheme processes each group of three consecutive sequence frames in two steps: 1) estimate motion between frames and 2) use motion vectors to get the final denoised current frame. A family of adaptive spatiotemporal L-filters is applied. A recursive implementation of these filters is used and compared with its nonrecursive counterpart. The motion trajectories are obtained recursively by a region-recursive estimation method. Both motion parameters and filter weights are computed by minimizing the kurtosis of error instead of mean squared error. Using the kurtosis in the algorithms adaptation is appropriate in the presence of mixed and impulsive noises. The filter performance is evaluated by considering different types of video sequences. Simulations show marked improvement in visual quality and SNRI measures cost as well as compared to those reported in literature.

Curvelet-based feature extraction with B-LDA for face recognition

In this paper, we propose a novel feature extraction scheme based on the multi-resolution curvelet transform for face recognition. The obtained curvelet coefficients act as the feature set for classification, and are used to train the ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA (BLDA) techniques. The proposed method CV-BLDA has been extensively assessed using different databases: the ATT, YALE and FERET, Tests indicate that using curvelet-based features significantly improves the accuracy compared to standard face recognition algorithms and other multi-resolution based approaches.

Color texture classification method based on a statistical multi-model and geodesic distance

A statistical multi-model for color texture classification is proposed.We use perceptual color spaces HSV and Lab as an alternative of RGB.We use the copula theory to build our models for luminance and chrominance.We derive a closed-form of geodesic distance between two copulas.We use the Bayesian classifier to assess the performance of our multi-model. In this paper, we propose a novel color texture classification method based on statistical characterization. The approach consists in modeling complex wavelet coefficients of both luminance and chrominance components separately leading to a multi-modeling approach. The copula theory allows to take into account the spatial dependencies which exist within the intra-luminance sub-bands via the luminance model M L , and also between the inter-chrominance subband coefficients via the chrominance model M C r . The multi-model, i.e. M L and M C r , is used to develop a Bayesian classifier based on the softmax principal. To derive the classifier, we propose a closed-form expression for the Rao geodesic distance between two copulas. Experiments on two sub-families of luminance-chrominance color spaces namely Lab and HSV have been carried out for a wide range of color texture databases. The combination of different statistical sub-models show that the multi-modeling performs better than some existing methods in term of classification rates.

Block based curvelet feature extraction for face recognition

In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed for face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA and Boosted LDA (BLDA). Two different muti-resolution transforms, Wavelet (DWT) and Contourlet, were also compared against the Block Based Curvelet algorithm. Experimental results on ORL, Yale and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.

Osteoporosis Diagnosis Using Fractal Analysis and Support Vector Machine

The objective of this paper lies on the characterization of osteoporosis disease using fractal analysis of X-Ray images. The method consists of a pre-processing step followed by a feature extraction based on the fractional Brownian motion (fBm) model. The Support Vector Machine (SVM) was used as a classifier to distinguish between two populations composed of Osteoporotic Patients (OP) and Control Cases (CC). Our proposed method achieved an accuracy classification rate of 95%, which means that it offers a good discrimination between OP patients and CC subjects.

Novel face recognition approach based on steerable pyramid feature extraction

In this paper, an efficient local appearance feature extraction method based steerable pyramid (S-P) is proposed for face recognition. Local information is extracted from S-P sub-bands using block-based statistics. The underlying statistics allow us to reduce the required amount of data to be stored. The obtained local features are combined at the feature and decision level to enhance face recognition performance. Experimental results on ORL, Yale and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.

Trabecular bone characterization using circular parametric models

Abstract Texture analysis of radiographic bone X-ray images presents a major challenge for pattern recognition and medical applications. Classifying such textures from osteoporotic and healthy subjects is a difficult task. In this paper, we propose a new approach combining wavelet decomposition and parametric circular models to capture the statistical behavior of phase coefficients. We demonstrate that, unlike the magnitude components, the wavelet phase coefficients convey local and structural information across scales and orientations which are of great interest for the study of trabecular bone texture. To assess how well the proposed circular models fit phase coefficients, the statistical test of Kuiper and graphical analysis Quantile–Quantile plots were used. The Support Vector Machine (SVM) and the Neural Network (NN) classifiers were used to evaluate the efficiency of the proposed models to classify two populations composed of osteoporotic patients and control subjects. Using Gabor filters and the Wrapped Cauchy model, an Area Under Curve (AUC) rate of 96.45% was achieved with the SVM classifier. To compare the performance of the proposed parametric approach to other non-parametric texture analysis techniques, the Receiver Operating Characteristic (ROC) analysis was performed. Results have proven that the proposed approach provides the best performance in terms of ROC curves.

Texture retrieval using mixtures of generalized Gaussian distribution and Cauchy-Schwarz divergence in wavelet domain

This paper presents a novel similarity measure in a texture retrieval framework based on statistical modeling in wavelet domain. In this context, we use the recently proposed finite mixture of generalized Gaussian distribution (MoGG) thanks to its ability to model accurately a wide range of wavelet sub-bands histograms. This model has already been relied on the approximation of Kullback-Leibler divergence (KLD) which hinders significantly the retrieval process. To overcome this drawback, we introduce the Cauchy-Schwarz divergence (CSD) between two MoGG distributions as a similarity measure. Hence, an analytic closed-form expression of this measure is developed in the case of fixed shape parameter. Otherwise, when the shape parameter is variable, two approximations are derived using the well-known stochastic integration with Monte-Carlo simulations and numerical integration with Simpsons rule. Experiments conducted on a well known dataset show good performance of the CSD in terms of retrieval rates and the computational time improvement compared to the KLD. HighlightsWe propose Cauchy Schwarz divergence CSD for texture retrieval.Wavelet coefficients are modeled by mixture of generalized Gaussians MoGG.We derive a closed-form of CSD between MoGG for fixed parameter shape.We use the Monte-Carlo approximation for CSD in the general case.We evaluate the performance in terms of the average retrieval rates and the computational time.

Texture classification based on the Generalized Gamma distribution and the Dual Tree Complex Wavelet Transform

This paper deals with stochastic texture modeling for classification issue. A generic stochastic model based on three-parameter Generalized Gamma (GG) distribution function is proposed. The GG modeling offers more flexibility parameterization than other kinds of heavy-tailed density devoted to wavelet empirical histograms characterization. Moreover, Kullback-leibler divergence is chosen as similarity measure between textures. Experiments carried out on Vistex texture database show that the proposed approach achieves good classification rates.