Author Topic: Enhancement of Person Identification using Iris Pattern  (Read 2512 times)

0 Members and 1 Guest are viewing this topic.

content.writer

  • Newbie
  • *
  • Posts: 48
  • Karma: +0/-0
    • View Profile
Enhancement of Person Identification using Iris Pattern
« on: April 23, 2011, 05:20:54 pm »
Quote
Author : Vanaja roselin.E.Chirchi, Dr.L.M.Waghmare, E.R.Chirchi
International Journal of Scientific & Engineering Research, IJSER - Volume 2, Issue 4, April-2011
ISSN 2229-5518
Download Full Paper -  http://www.ijser.org/onlineResearchPaperViewer.aspx?Enhancement_of_Person_Identification_using_Iris_Pattern.pdf

Abstract — The biometric person identification technique based on the pattern of the human iris is well suited to be applied to access control. Security systems having realized the value of biometrics for two basic purposes: to verify or identify users. In this busy world, identification should be fast and efficient. In this paper we focus on an efficient methodology for identification and verification for iris detection using Haar wavelet and the classifier used is Minimum hamming distance, even when the images have obstructions, visual noise and different levels of illuminations.

Index Terms—Biometrics, Iris identification, Haar wavelet, occluded images, veriEye.

1.   Introduction
Biometrics which refers to identifying an individual by his or her physiological or behavioral characteristics has capability to distinguish between authorized user and an imposter. An advantage of using biometric authentication is that it cannot be lost or forgotten, as the person has to be physically present during at the point of identification process [9].Biometrics is inherently more reliable and capable than traditional knowledge based and token based techniques. The commonly used biometric features include speech, fingerprint, face, Iris, voice, hand geometry, retinal identification, and body odor identification [10] as in Fig.1

   
Fig. 1: Examples of Biometrics ( Download Full Paper to View )

To choose the right biometric to be highly fit for the particular situation, one has to navigate through some complex vendor products and keep an eye on future developments in technology and standards. Here comes a list of Biometrics with comparatives:

Facial Recognition: Facial recognition records the spatial geometry of distinguishing features of the face. Different vendors use different methods of facial recognition, however, all focus on measures of key features of the face. Facial recognition has been used in projects to identify card counters or other undesirables in casinos, shoplifters in stores, criminals and terrorists in urban areas. This biometric system can easily spoof by the criminals or malicious intruders to fool recognition system or program. Iris cannot be spoofed easily.

Palm Print: Palm print verification is a slightly modified form of fingerprint technology. Palm print scanning uses an optical reader very similar to that used for fingerprint scanning; however, its size is much bigger, which is a limiting factor for use in workstations or mobile devices.
Signature Verification: It is an automated method of examining an individual’s signature. This technology is dynamic such as speed, direction and pressure of writing, the time that the stylus is in and out of contact with the “paper”. Signature verification templates are typically 50 to 300 bytes. Disadvantages include problems with long-term reliability, lack of accuracy and cost.

Fingerprint: A fingerprint as in Fig.1 recognition system constitutes of fingerprint acquiring device, minutia extractor and minutia matcher. As it is more common biometric recognition used in banking, military etc., but it has a maximum limitation that it can be spoofed easily. Other limitations are caused by particular usage factors such as wearing gloves, using cleaning fluids and general user difficulty in scanning.

Iris Scan: Iris as shown in Fig.2 is a biometric feature, found to be reliable and accurate for authentication process comparative to other biometric feature available today. As a result, the iris patterns in the left and right eyes are different, and so are the iris patterns of identical twins. Iris templates are typically around 256 bytes. Iris scanning can be used quickly for both identification and verification applications because of its large number of degrees of freedom. Iris as in Fig. 2 is like a diaphragm between the pupil and the sclera and its function is to control the amount of light entering through the pupil. Iris is composed of elastic connective tissue such as trabecular meshwork. The agglomeration of pigment is formed during the first year of life, and pigmentation of the stroma occurs in the first few years [7][8].

Fig. 2:  Structure of Eye ( Download Full Paper to View )

The highly randomized appearance of the iris makes its use as a biometric well recognized. Its suitability as an exceptionally accurate biometric derives from [4]:
i.   The difficulty of forging and using as an imposter person;
ii.   It is intrinsic isolation and protection from the external environment;
iii.   It’s extremely data-rich physical structure;
iv.   Its genetic properties—no two eyes are the same. The characteristic that is dependent on genetics is the pigmentation of the iris, which determines its color and determines the gross anatomy. Details of development, that are unique to each case, determine the detailed morphology;
v.   its stability over time; the impossibility of surgically modifying it without unacceptable risk to vision and its physiological response to light, which provides a natural test against artifice.
After the discovery of iris, John G. Daugman, a professor of Cambridge University [8] ,[9], suggested an image-processing algorithm that can encode the iris pattern into 256 bytes based on the Gabor transform.
In general, the iris recognition system is composed of the following five steps as depicted in Fig. 3 According to this flow chart, preprocessing including image enhancement.

2.   Image Acquisition.
An image of the eye to be analyzed must be acquired first in digital form suitable for analysis. In further implementation we will be using Chinese academy of science-Institute of automation (CASIA) iris image database available in the public domain [7].

Read More: http://www.ijser.org/onlineResearchPaperViewer.aspx?Enhancement_of_Person_Identification_using_Iris_Pattern.pdf