Hela Mahersia

Improving Iris Recognition Performance Using Quality Measures

Biometric methods, which identify people based on physical or behavioural characteristics, are of interest because people cannot forget or lose their physical characteristics in the way that they can lose passwords or identity cards. Among these biometric methods, iris is currently considered as one of the most reliable biometrics because of its unique texture‘s random variation. Moreover, iris is proved to be well protected from the external environment behind the cornea, relatively easy to acquire and stable all over the person’s life. For all of these reasons, iris patterns become interesting as an alternative approach to reliable visual recognition of persons. This recognition system involves four main modules: iris acquisition, iris segmentation and normalization, feature extraction and encoding and finally matching. However, we noticed that almost all the iris recognition systems proceed without controlling the iris image’s quality. Naturally, poor image’s quality degrades significantly the performance of the recognition system. Thus, an extra module, measuring the quality of the input iris, must be added to ensure that only “good iris” will be treated by the system. The proposed module will be able to detect and discard the faulty images obtained in the segmentation process or which not have enough information to identify person. In literature, most of evaluation quality methods have developed indices to quantify occlusion, focus, contrast, illumination and angular deformation. These measurements are sensitive to segmentation errors. Only few methods have interested on the evaluation of iris segmentation. This chapter aims to present, firstly a novel iris recognition method based on multi-channel Gabor filtering and Uniform Local Binary Patterns (ULBP), then to define a quality evaluation method which integrates additional module to the typical recognition system. Proposed method is tested on Casia v3 iris database. Our experiments illustrate the effectiveness and robustness of ULBP to extract rich local and global information of iris texture when combined with simultaneously multi-blocks and multi-channel method. Also, obtained results show an improvement of iris recognition system by incorporating proposed quality measures in the typical system. This chapter is organized as follows: Section 2 describe in details the proposed iris recognition system. The further represents the quality evaluation method. In section 4, we expose experiments, results and comparison. Finally, the conclusion is given in section 5.

Using multiple steerable filters and Bayesian regularization for facial expression recognition

Abstract Facial expression recognition has recently become a challenging research area. Its applications include human–computer interfaces, human emotion analysis, and medical care and cure. In this paper, we present a new challenging method to recognize seven universal emotional expressions, which are happiness, neutral, angry, disgust, sadness, fear and surprise. In our approach, we identify the user׳s facial expressions from the input images, using statistical features extracted from the steerable pyramid decomposition, and classified with a Bayesian regularization neural network. The evaluation of the proposed approach in terms of recognition accuracy is achieved using two universal databases, the Japanese Female Facial Expression database and the Cohn–Kanade facial expression database. The overall accuracy rate reaches 93.33% for the first database and is about 98.13% for the second one. These results show the effectiveness of the steerable proposed algorithm.

A Novel Content Image Retrieval Method Based on Contourlet

Content-based image retrieval is an active and fast advancing research area since the 1990s as a result of advances in the Internet and new digital image sensor technologies. However, many challenging research problems continue to attract researchers from multiple disciplines. Content-based image retrieval uses the visual contents of an image as features to represent and index the image to be searched from large scale image databases. The quality of the selected features relies mainly on the degree of the invariance property that is ensured under acceptable manipulations. This paper proposes an efficient method for compactly representing color and texture features and combining them for image retrieval. The performance of retrieval based on these compact descriptors obtained by the proposed techniques is analyzed and tested on wang database images yielding satisfactory accuracy rates. A comparative study demonstrated that the developed feature extraction scheme outperformed the other schemes being compared with.

Iris Recognition using Steerable Pyramids

This work presents a new iris recognition method based on steerable pyramid transform. This method consists of four steps: localization, normalization, features extraction and matching. After locating the iris boundaries by Hough Transform, normalization is operated by unwrapping the circular ring and isolating the noisy regions. Steerable pyramid filters are then used to capture orientation details from the iris texture. The features are extracted on each filtered sub-image to form a fixed length feature vector which will be compared to other vectors in the matching step. This technique has been tested on infrared light iris images. It has been compared, in both identification and verification modes, to known methods.

Denoising CT Images using wavelet transform

Image denoising is one of the most significant tasks especially in medical image processing, where the original images are of poor quality due the noises and artifacts introduces by the acquisition systems. In this paper, we propose a new image denoising scheme by modifying the wavelet coefficients using soft-thresholding method, we present a comparative study of different wavelet denoising techniques for CT images and we discuss the obtained results. The denoising process rejects noise by thresholding in the wavelet domain. The performance is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). Finally, Gaussian filter provides better PSNR and lower MSE values. Hence, we conclude that this filter is an efficient one for preprocessing medical images.

A New CAD System for Breast Microcalcifications Diagnosis

Breast cancer is one of the most deadly cancers in the world, especially among women. With no identified causes and absence of effective treatment, early detection remains necessary to limit the damages and provide possible cure. Submitting women with family antecedent to mammography periodically can provide an early diagnosis of breast tumors. Computer Aided Diagnosis (CAD) is a powerful tool that can help radiologists improving their diagnostic accuracy at earlier stages. Several works have been developed in order to analyze digital mammographies, detect possible lesions (especially masses and microcalcifications) and evaluate their malignancy.nIn this paper a new approach of breast microcalcifications diagnosis on digital mammograms is introduced. The proposed approach begins with a preprocessing procedure aiming artifacts and pectoral muscle removal based on morphologic operators and contrast enhancement based on galactophorous tree interpolation.nThe second step of the proposed CAD system consists on segmenting microcalcifications clusters, using Generalized Gaussian Density (GGD) estimation and a Bayesian back-propagation neural network.nThe last step is microcalcifications characterization using morphologic features which are used to feed a neuro-fuzzy system to classify the detected breast microcalcifications into benign and malignant classes.

Ensemble Neurocomputing Based Oil Price Prediction

In this paper, we investigated an ensemble neural network for the prediction of oil prices. Daily data from 1999 to 2012 were used to predict the West Taxes, Intermediate. Data were separated into four phases of training and testing using different percentages and obtained seven sub-datasets after implementing different attribute selection algorithms. We used three types of neural networks: Feed forward, Recurrent and Radial Basis Function networks. Finally a good ensemble neural network model is formulated by the weighted average method. Empirical results illustrated that the ensemble neural network outperformed other models.

How to extend the use of the discrete orthogonal stockwell transform to image watermarking

In this paper, we explore the use of the discrete orthogonal Stockwell transform (DOST) in image watermarking by proposing a blind medical image watermarking (MIW) method based on multi-resolution representation that proceeds by embedding the watermark within the high frequency subbands of the 2D DOST representation of the cover image. Experimental results show that the proposed method improved the transparency of watermarking artifacts in comparison with other works and achieved a good compromise between fidelity and capacity.

Blind lossless medical image watermarking in the DOST domain using difference expansion

In this paper, we extend the use of the Discrete Orthogonal Stockwell Transform (DOST) by introducing a blind reversible and fragile medical image watermarking (MIW) method for image integrity checking. 2D DOST is first applied to the host image, the watermark is inserted in the high frequency coefficients based on the Difference Expansion (DE) technique. The extraction process provides host image retrieval after watermark removal. Experimental results show that the method achieved a high visual quality for marked images resulting in a better compromise between fidelity a nd capacity.

Breast masses diagnosis using supervised approaches

Computer Aided Diagnosis (CAD) is usually used to assist radiologists while interpreting mammograms and help them improving breast cancer diagnosis accuracy at earlier stages. One of the main breast cancer early indicators is the presence of masses. CAD systems main target is to detect eventual masses from digital mammograms characterize them and evaluate their malignancy. In this paper, we introduce a new approach of breast masses diagnosis on digital mammograms, which begins with a preprocessing step where artifacts and pectoral muscle are removed and then the contrast is enhanced. The second step consists on segmenting breast masses, using Generalized Gaussian Density (GGD) estimation and a Bayesian backpropagation neural network. The last step is masses characterization using a combination of morphologic and textural features which are exploited to classify the segmented masses into benign and malignant classes, using a neuro-fuzzy system (ANFIS). The proposed CAD system was tested on the MIAS database and masses detection rate has reached 97.08% with the GGD analysis and bayesian back-propagation neural network. 97% of these detected masses were correctly classified with an ANFIS system.