Hand Vein Biometric Authentication System

Hand Vein Biometric Authentication System

ABSTRACT

This paper presents a hand vein authentication system using fast correlation of hand vein patterns. Biometric identification is an important security application that requires non-intrusive capture and real-time processing. Security systems based on fingerprints and retina patterns have been widely developed, but can be easily falsified. As a kind of biometric feature authentication system, hand vein recognition has more merits than others. So it has a vast foreground. In this project, a new algorithm based on local threshold segmentation for extracting features of hand vein pattern features is studied. Verification performance statistical parameters were estimated such as: Genuine Accept Rate (Sensitivity), Genuine Reject Rate (Specificity), False Accept Rate (FAR), False Reject Rate (FRR), Efficiency and Receiver Operating Curve (ROC). System overall performance (overall efficiency) was found to be 99.88% at threshold (matching ratio) equal 78. At this maximum efficiency the Sensitivity obtained is 92.16%, the Specificity is 99.97%, FAR is 0.03%, and FRR is 1.24%.

INTRODUCTION

ASSOCIATING an identity with an individual is called Personal identification. The problem of resolving the identity of a person can be categorized into two types of problems; verification and identification. Verification (authentication) refers to the problem of confirming or denying a person’s claimed identity (Am I who I claim I am?).Identification (Who am I?) refers to the problem of establishing a person’s identity. Automatic human identification has become an important issue in today’s information and network-based society. The techniques for automatically identifying an individual based on his/her physiological or behavioral characteristics are called biometrics, which provides an answer to this need. Biometric techniques fall into two categories: physiological behavioral categories.

Common physiological biometrics include face, eye (retina or iris), finger (fingertip, thumb, finger length or pattern), palm (print or topography), and geometry, back of the hand vein pattern or thermal images. Behavioral biometrics includes voiceprints, handwritten signatures, and keystroke/signature dynamics.

Personal verification has become an important and high demand technique for security access systems in the last decade. Shape of the subcutaneous vascular tree of the back of the hand contains information that is capable of authenticating the identity of an individual [1-5] to a reasonable accuracy for automatic personal authentication purposes. The shape of the finger vein patterns and its use for identification purpose was proposed by Miura et al. [11]. The infrared region is of special advantage since the skin tissue is relatively transparent and the blood absorbs infrared light well. Hence, the veins background contrast is higher than the visible area. Since the arrival of fairly low cost CCD cameras and computer power, it seems straightforward to try to consider these technologies . Normally, black and white CCD cameras are also sensitive in the near infrared region, so a filter blocking the visible light is all that is needed on the camera. Proper lighting is of course essential to obtain even illumination on the skin surface. There are many research attempts for the extraction, segmentation and tracing of subcutaneous peripheral venous patterns , its main aim is to make data reduction and noise suppression for good diagnostic purposes and for making some quantitative measurements like lengths and diameters for the extracted vessel segments. These techniques are based on mathematical morphology and curvature (veins direction) evaluation for the detection of vessel patterns in a noisy environment. Researchers in hand vein biometrics [1-5,] had a satisfactory result for either verification or identification purposes, regardless of the difference in datasets size, methods, or vein similarities used. The vein tree detection stage includes four consecutive sub stages, which are hand region segmentation (i.e. region of interest localization and background elimination), smoothing and noise reduction, local thresholding for separating veins, and post processing. In this paper we propose a design of a hand vein biometric authentication system performing a fast spatial correlation method for hand vein patterns matching.

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