Raouia Mokni

Identification and verification system of offline handwritten signature using fractal approach

In this paper, we propose a new system for identification and verification of offline handwriting signature. The identification allows knowing the owner of such a signature, whereas the verification allows verifying the authenticity of a given signature. In recognition systems, the phase of feature extraction represents the most pertinent step. Therefore, we opted to use the fractal approach which is very sophisticated for objects with irregular forms. In this work, we have implemented three methods for calculating fractal dimensions. Experimental results have been assumed on a large database, namely, “FUM-PHSDB” showing promising result and demonstrating the effectiveness of the proposed system. These experiments show an identification rate of 95% and a verification rate of 83%.

Pre-processing and extraction of the ROIs steps for palmprints recognition system

The Palmprint recognition is a reliable method to identify the person. Actually, several researchers are interested in this method in the last few years thanks to its intense contributions to public security. This paper aims to present a biometric system based on a new approach that focuses on the dominant features of palmprint for recognition. It also introduces new techniques to locate the Region Of Interest (ROI) of the hand by using the Chinese Database, which is called CASIA-Palmprint. We used various methods which employ the image processing techniques including the use of the Otsus method to binarize the original image, the boundary extraction, the smoothing determination to remove the noise and to eliminate the holes curve and the detection of the centroid to extract the key points applying the Euclidean distance. These methods are used to finally extract the ROIs from the left and right hand. We resized the ROIs to 150 × 150 and we rotated it according to the detected angle. Experimental results were obtained on the “CASIA-Palmprint” database show promising results and demonstrate the effectiveness of the proposed system. These results obtained are compared with some of the state of the arts techniques.

Combining shape analysis and texture pattern for palmprint identification

We propose an efficient method for principal line extraction from the palmprint and geometric framework for analyzing their shapes. This representation, along with the elastic Riemannian metric, seems natural for measuring principal line deformations and is robust to challenges such as orientation variation and re-parameterization due to pose variation and missing part, respectively. The palmprint texture is investigated using the fractal analysis; thus the resulting features are fused with the principal line features. This framework is shown to be promising from both – empirical and theoretical – perspectives. In terms of empirical evaluation, our results match or improve the state-of-the-art methods on three prominent palmprint datasets: PolyU, CASIA, and IIT-Delhi, each posing a different type of challenge. From a theoretical perspective, this framework allows fusing texture analysis and shape analysis.

Biometric Palmprint identification via efficient texture features fusion

Recently, personal identification, which is based on the palmprint texture features analysis, has widely attracted the attention of several researchers and has gained a great popularity in the pattern recognition field. In this paper, we present a novel methodology based on texture information extracted from palmprint. Firstly, we propose an algorithm to robustly locate the Region Of Interest (ROI) of the hand. Secondly, we combine multiple descriptors to extract the palmprint texture information, which are Gray-Level Co-occurrence Matrix (GLCM) and the Gabor filters using feature level fusion. These descriptors have been broadly applied in various tasks, specifically in the image processing domain to analyze the image texture. Then, we apply the generalized discriminant analysis (GDA) to reduce the length of the feature vectors and their redundancies. Finally, we classify these final resulting features by developing the SVM method which supports several kernel functions to reach a best recognition rate. We have conducted extensive experiments on the “CASIA-Palmprint” and “PolyU-palmprint” datasets. The obtained results of the proposed approach provide promising results compared to other well-known state-of-the-art approaches.

Palmprint Biometric System Modeling by DBC and DLA Methods and Classifying by KNN and SVM Classifiers

Biometric technology is an automatic personal identification method based on physical or behavioral characteristics of the individuals. Among of the physical characteristics, palmprint is useful in various applications such as forensic science access control, thus resulting in an increasing of research interest. In this paper, we explore a new methodology focused on integrating the fractal and Multi-fractal techniques for human identification based on extracting the texture pattern features. Therefore, we extract the palmprint texture information based on the calculation of the fractal dimensions using the Differential Box Counting (DBC) and the Diffusion Limited Aggregates (DLA) methods corresponding to the Fractal and Multi-Fractal techniques respectively. These methods have been broadly applied in image processing fields to estimate the fractal dimensions of an image as important parameters for analyzing the irregular shapes of the texture image. The proposed method produces encouraging recognition rates by 94.02 % and 93.44 % when tested on benchmark databases “CASIA-Palmprint” and “IITD-Palmprint” respectively. The performance of our method is compared with palmprint recognition accuracy gained from well-known state-of-the-art palmprint recognition, producing favorable results.

Novel palmprint biometric system combining several fractal methods for texture information extraction

This paper presents a new method to recognize the persons identity through their palmprints. Palmprint recognition is among the most reliable physiological characteristics that can be used especially in forensic applications thanks to its simplicity and its ease of use, its user friendliness and high identification reliability. Accordingly, it has gained great popularity within the pattern recognition field over the past three decades. In this paper, we suggest a new approach for personal identification based on palmprint features extracted using the various methods of fractal theory. These methods have been broadly applied in image processing fields to estimate the fractal dimensions of an image as an important parameter for the analysis of objects of irregular shapes of the texture image. The novelty of this approach is two-fold. On the one hand, we apply the Box counting (BC), the Mass Radius (MS) and the Cumulative Intersection (CumInt) methods to extract the palmprint texture information. On the other hand, the combination of efficient information from the three descriptors has been presented in order to make identification system more efficient and achieve better performances. Then, we explore such texture information features by using classical machine learning techniques: the K-Nearest Neighbor (KNN), the Support Vector Machine (SVM) and the Multiclass Random Forest classification algorithms. The results of the experiments conducted on two large datasets show that our proposed method gives better recognition rates of about 96.35% for CASIA-Palmprint dataset and 95.98% for IITD-Touchless-Palmprint dataset. These results obtained are compared to other well-known state-of-the-art approaches.

Offline Arabic Handwritten recognition system with dropout applied in Deep networks based-SVMs

As a machine learning algorithms, deep learning algorithms developed in recent years, have been successfully practiced in many fields of computer vision, like face recognition, object detection and image classification. These Deep algorithms look for drawing out a very performing representation of the data, among which image and speech, through multi-layers in a deep hierarchical structure. In this study, a deep learning model based on Support Vector Machine (SVM) named Deep SVM (DSVM) is represented. We applied the dropout technique on the Deep SVM (DSVM). It is worth noting that this model has an inherent capacity to choose data points crucial to classify good generalization capacities. The deep SVM is built by a stack of SVMs permitting to extracting/learning automatically features from the raw images and to realize classification, too. We chose and tested the Multi-class Support Vector Machine with an RBF kernel, as non-linear discriminative features for classification, on Handwritten Arabic Characters Database (HACDB). Further to these advantages, our model is safeguarded against over-fitting because of strong performance of dropout. Simulation outcomes prove the efficiency of the suggested model.

Palmprint recognition through the fractal dimension estimation for texture analysis

Palmprint is a human physiological feature which can distinguish and identify one person from another. In the palmprint recognition biometric systems, the feature extraction is considered as the most important step. In this paper, we use the fractal approach which is both a very advanced and sophisticated method in order to extract the palmprint texture information features. This approach has been widely used in recent years being considered as an active research area in the image processing field. Therefore, we have implemented a new technique to extract the palmprint texture features: the texture analysis basing on the fractal dimension estimated via the box-counting method or TAFD-BC. Experimental results on the PolyU 2D Palmprint database prove that our proposed approach produces promising and favourable results compared to other well-known state-of-the-art techniques.

Multiset Canonical Correlation Analysis: Texture Feature Level Fusion of Multiple Descriptors for Intra-modal Palmprint Biometric Recognition

This paper describes a novel intra-modal feature fusion for palmprint recognition based on fusing multiple descriptors to analyze the complex texture pattern. The main contribution lies in the combination of several texture features extracted by the Multi-descriptors, namely: Gabor Filters, Fractal Dimension and Gray Level Concurrence Matrix. This means to their effectiveness to confront the various challenges in terms of scales, position, direction and texture deformation of palmprint in unconstrained environments. The extracted Gabor filter-based texture features from the preprocessed palmprint images to be fused with the Fractal dimension-based-texture features and Gray Level Concurrence Matrix-based texture features using the Multiset Canonical Correlation Analysis method (MCCA). Realized experiments on three benchmark datasets prove that the proposed method surpasses other well-known state of the art methods and produces encouraging recognition rates by reaching 97.45% and 96.93% for the PolyU and IIT-Delhi Palmprint datasets.