Randa Boukhris Trabelsi

Hand vein recognition system with circular difference and statistical directional patterns based on an artificial neural network

In this article, a novel hand vein pattern recognition process for human identification is presented. Hand vein characteristics can be considered as more reliable in biometric domain compared with other biometric characteristics, such as palmprint and fingerprint, because veins are located in volume, making features more robust to test conditions. In this paper, a rotation invariant texture descriptor called Circular Difference and Statistical Directional Patterns (CDSDP) is proposed to extract hand vein patterns. Its histogram is considered as attribute vector. The CDSDP is a surrounding circular difference with weights incorporating the statistical directional information of vessels. Experimental results show that the proposed descriptor based on CDSDP has better performance than the previous descriptors used in local binary patterns (LBP). The proposed method gives an Identification Rate (IR) of 99.8 % and an Error Equal Rate (EER) of 0.01 %. Furthermore, the average processing time of the proposed method is 5.2ms for one hand vein posture, which satisfies the criterion of a real time hand vein recognition system.

A Novel Biometric System Based Hand Vein Recognition

As a reliable and universal biometric characteristic, hand vein identification has attracted many interested researchers. The hand vein identification system exhibits several excellent advantages in the biometric domain because it meets the increasing demand of accuracy and robustness. In this paper, we propose a new biometric recognition system based on hand vein features. The detection and extraction of the region of interest is based on Voronoi Decomposition. Furthermore, contrast enhancement is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Starting with the basic Gaussian Matched Filter (GMF) and its variant, we propose a new technique called the Improved Gaussian Matched Filter method (IMPGMF) surmounting the false detection of hand vessels with the traditional GMF. Feature points are then detected based on ending and bifurcation structures in the image map obtained with the proposed IMPGMF method and taken as signature for our biometric system. Then, Artificial Neural Networks (ANN) are used for the classification step. In the validation step, we used a 1500 hand vein image from the BOSPHORUS database. The Error Equal Rate is 0.01 % and the Area Under curve of the corresponding system is approximately 0.98, demonstrating a very high security level.

Multimodal biometric system based palmprzint and IRIS

Biometric technology identifies individuals automatically using their physiological and/or behavioral characteristics. The uni-modal biometric systems can recognize a person using a single biometric modality, but can not guarantee with certainty the proper identification. Moreover, these systems can present a variety of problems such as noisy data, the intra-class and unacceptable error rates. Some of these limitations can be addressed by the deployment of multimodal biometric systems that incorporate elements presented by multiple sources of information. In this paper, we present a new fusion technique of biometric signatures from the palmprint and iris. For iris recognition, we employ fractal analysis method, while a Local binary patterns (LBP) is used for palmprint recognition. Finally, the scores are fused to authenticate the identity using the new fusion scheme.

A bi-modal palmvein palmprint biometric human identification based on fusing new CDSDP features

Uni-modal analysis of palmprint and palmvein has been investigated for human recognition. One of the problems encountered with such system is that the Uni-modal biometric is less perfect, reliable and vulnerable to spoofing, as the data can be imitated or forged. In this paper, we present a multi-modal Personal identification system using palmprint and palmvein images with their fusion applied at feature level. The feature vectors of palmprint and palmvein images are extracted with Circular Difference and Statistical Directional Patterns descriptor. Experimental results show that the proposed system provides a better representation and achieves lower error rates in palm recognition. Furthermore, the proposed multimodal method outperforms any of its individual modality.

A New Dynamic ROI Extraction Method for Hand Vein Images

The region of interest (ROI) extraction is important in hand vein recognition system. The main challenges for accurate extraction of the vein region are to overcome variability in hand size, lighting conditions, orientation, appearance, noisy background, and non-uniform grey levels in foreground region. Here, we propose a new dynamic hand vein ROI extraction, preserving the whole vein area. A hand segmentation process robust to the mentioned challenges, contributing to an accurate definition of hand edge delimitations is proposed. Our approach is validated on both dorsal vein Bosphorus database and palm vein Vera database. Our proposed method accuracy is ~98% for Bosphorus database and 90% for Vera database. To illustrate the efficiency of the proposed ROI extraction, we insert it as a first block in a hand vein recognition system. Then, a comparison study at system level with recent approaches is carried on, showing an improvement of the whole system area under the curve by a rate of 12% and 2% for Bosphorus and Vera databases, respectively. The speed performances demonstrate a mean run time of 0.73 s for Bosphorus database and 1.2 s for Vera database, proving that the proposed method can be conveniently used on a real-time application.

Age and Gender Classification from Finger Vein Patterns

The main goal of this paper is to build a system able to recognize the age range and the gender of individuals from their venous network characteristic. Accordingly, we develop an algorithm able to detect changes related to aging. Proposed age and gender recognition system is composed by 4 key steps: image acquisition, image preprocessing, feature extraction and age/gender classification. Image preprocessing is established by ROI extraction and image enhancement. ROI extraction separates the informative region from finger vein image. For image enhancement, we use Guided Filter based Singe Scale Retinex (GFSSR) method. In feature extraction step, we implement the LBP descriptor in order to characterize venous texture from finger veins. Our study is based on MMCBNU_6000 finger vein database. Experimental results prove that extracted attributes from finger vein can define the gender and the age class. Proposed age and gender classification process gives a recognition rate of 98% for gender classification and a recognition rate of 99.67%, 99.78% and 97.33% for respectively 2, 3 and 4 classes, for age classification.