Hanene Trichili

Multimodal Biometric System Based on Fingerprint Identification and Iris Recognition

Mono modal biometric systems encounter a variety of security problems and present sometimes unacceptable error rates. Some of these drawbacks can be overcome by setting up multimodal biometric systems. Multimodal biometrics consists in combining two or more biometric modalities in a single identification system to improve the recognition accuracy. However features of different biometrics have to be statistically independent. This paper proposes a multimodal biometric systems using fingerprint and iris recognition.

Combined local features selection for face recognition based on Naïve Bayesian classification

Face recognition is a very popular biometric solution in the literature. Several solutions are presented to meet the needs of individuals verification or identification. There are three types of face recognition approaches: local, global and hybrid. In this paper, we proposed a local approach for face recognition based on combined features selection methods like Genetic algorithm, Gramdt Shmidt algorithm, mRmR features selection algorithm and naïve Bayesian classifier. Our proposed approach will be compared with some face recognition systems based on global features. A comparative study is given in this paper based on Recognition rates and Execution times. Our Face recognition system, which is based on naïve Bayesian classifier and tested on ORL face database, has showed 78.75% recognition rate and interesting execution times compared to global approaches.

A survey and evaluation of edge detection operators application to medical images

One of the objectives of image analysis is to extract its dominating information. Thus we use segmentation to associate a stamp to each pixel according to the carried information (gray level or color) and its specific distribution in the image. Thereby, the segmentation of the image is defined as being the low level step of processing that extracts and describes present significant objects in a scene, the most often in the form of regions or edges. In the literature, different methods have been elaborated in order to detect image edges. They are gathered in two families: on the one hand methods privileging an approach by border (derivative, surfaces, and morphological methods) named the edge approach; on the other hand those privileging an approach by regions (Markovian and structural methods). In this work, we are interested in the different methods using the edge approach for the image segmentation. Many image segmentation techniques are available. We describe derivative methods, optimal filtering, and segmentation for color images.

Bimodal biometric system based on SIFT descriptors of hand images

Hand shape biometry is among the most popular biometrics employed to characterize a person in forensic applications, due to its simplicity of use and acceptance of individuals. However, this modality presents weaknesses which may make system inaccurate. In fact, people from the same family or twins may have related hand features. Therefore, the performance of the hand verification process depends highly on the hand descriptors. In this paper, we propose a new approach for personal verification combining hand shape and palmprint features extracted using the Scale Invariant Feature Transform (SIFT). This transform was improved its high distinction and efficiency in many applications especially in object recognition and video tracking. Our experiments on IITD hand database demonstrate promising results by fusing at matching level score the hand shape and palmprint modalities. These results are comparable with similar bimodal identification methods.

Face recognition based on geometric features using Support Vector Machines

Face Recognition is among the most widely studied problems in computer vision and Pattern Recognition. Face has many advantages like permanence, accessibility and universality. It is still now not solved in literature. Several approaches are proposed to overcome with problems including; changing posed, emotional states, and illumination variation, etc. Geometric approaches which used as example distance between noses, eyes, mouth are still less efficient compared to holistic approaches. However, it provide and additional local information such as shape of local facial parts, face unit action, etc. The major problem of these approaches is to select the most relevant distances that can differentiate human faces. In this paper, we propose a bag of geometrical features based face recognition approaches using Support Vector Machines (SVM), Genetic Algorithm (GA) and minimum redundancy maximum relevance (mRmR) with Mutual Information Difference (MID) and Mutual Information Quotient (MIQ). Support Vector Machine Classifier (SVM) based on linear, radial basis function and multi layer Perceptron kernels is performed on the two benchmarks of facial databases ORL and Caltech Faces. Linear kernel based SVM classification using 10 selected distances by Genetic Algorithm (GA) ranks top the list of kernels conducted in our experimental study.

Novel hand biometric system using invariant descriptors

Biometrics-based hand authentication is among the most popular biometrics used to automatically characterize a person especially in forensic applications. Hand recognition systems are able to confirm or deny the identity of a claimed person because they do not cause anxiety for the users. However, different individuals may have almost similar hands. Therefore, the performance of the hand verification process depends highly on the hand descriptors. In this paper, we propose a new approach for personal verification based on Scale Invariant Feature Transform (SIFT). This transform proved its high distinction and efficiency in many applications especially in object recognition and video tracking. Two public databases have been used to evaluate performances. Experimental results show promising recognition rates by achieving 94% for IITD hand database and 98% for Bosphorus hand database.

MLP Neural Network for face recognition based on Gabor Features and Dimensionality Reduction techniques

Face recognition is very used in biometric market for many reasons. Face can be detected at distance without users implication in enrollment process. Face recognition systems are still expanded in video surveillance areas to control access. This paper presents an experimental study on some proposed face recognition approaches by building systems with different techniques for features extraction and classification. To validate comparison between proposed approaches, we use three face image databases ORL, Caltech Faces and Face94. We demonstrated in this paper that Gabor Features applied with Linear Discriminate analysis to reduce size of dataset classified with MLP Neural Network ranks top the list of proposed approaches as well as many works done in literature.

Bimodal biometric system for hand shape and palmprint recognition based on SIFT sparse representation

Biometric-based hand modality is considered as one of the most popular biometric technologies especially in forensic applications. In this paper, a bimodal hand identification system was proposed based on Scale Invariant Feature Transform (SIFT) descriptors, extracted from hand shape and palmprint modalities. A local sparse representation method was adopted in order to represent images with high discrimination. Moreover, fusion was performed at feature and decision levels using a cascade fusion in order to generate the final identification rate of our bimodal system. Our experiments were applied on two hand databases: Indian Institute of Technology of Delhi (IITD) hand database and Bosphorus hand database containing, respectively, 230 and 615 subjects. The results show that the proposed method offers high accuracies compared to other popular bimodal hand biometric methods over the two hand databases. The correct identification rate reaches 99.57 % which is competitive compared to systems existing in the literature.

Iris verification system based on curvelet transform

The performance of the iris verification process highly depends on its extractor of iris features. So, to reduce the dimensionality of the iris image and improve the recognition rate, an iris features extraction method based on curvelet transform is proposed and presented in this paper. Thus, our paper focuses on presenting of our curvelet-based iris features extraction method. This method consists of two steps: decompose images into set of sub-bands by the curvelet transform and automatic extraction of the most discriminative features of these sub-bands. An extensive experimental results show that the proposed method is effective and encouraging.

Hand verification system based on multi-features fusion

Human hand is a physiological biometric trait employed in order to characterize and identify a person. It is considered as one of the most popular biometric technologies especially in forensic applications, due to its high users acceptance compared to other biometric technologies. In this paper, we propose a hand biometric system for personal identity verification, fusing multiple features of the hand at matching score level. In fact, shape and texture are extracted from hand, fingers and palmprint in order to represent the hand image of each person. In the feature extraction strategy, the scale Invariant Feature Transform (SIFT) is extracted from the hand image to describe local invariant features of the hands contour and also extracted from fingers images. In the other hand, Gabor filters are extracted from palmprint images to describe the texture of the hand. The main advantage of these two descriptors (SIFT and Gabor) is that features extracted are invariant to rotation, translation, scale and lighting changes. Personal verification was performed by fusing similarity scores achieved from the hand shape, the fingers and the palmprint. Experimental results show good performances (EER=1.95%) in hand verification using a database containing 230 different subjects.