Biometrics behaviour

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Biometrics refers to methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. In information technology, in particular, biometrics is used as a form of identity access management and access control. It is mainly used to identify individuals. Mainly, biometric project passes through the stages of acquisition, preprocessing, feature extraction and matching of an image.

Iris recognition is a method of biometric authentication that uses pattern-recognition techniques based on high-resolution images of the irides1 of an individual's eyes. It is regarded as the most reliable and accurate biometric identification system available as two irides are never similar. The iris only changes in very small degrees over time, making it ideal for recognition. The iris is an internal planar organ that is well protected from damage.

Most Iris recognition system uses Daugman Algorithm. Daugman's algorithm is based on applying an integro-differential operator to find the iris and pupil contour. After extracting required iris pattern, it is encoded into a bit-wise biometric template. This is compared with the database patterns for matching using Hamming distance.

Iris recognition efficacy is rarely impeded by glasses or contact lenses. Iris technology has the smallest outlier (those who cannot use/enroll) group of all biometric technologies. The only biometric authentication technology designed for use in a one-to-many search environment, a key advantage of iris recognition is its stability, or template longevity, as, barring trauma, a single enrollment can last a lifetime. Iris scanner is better than its predecessor retinal scanner as user need not stand very near to the scanner.

The advantages and disadvantages of using human iris for Biometric Identification are as below:


The iris of the eye has been described as the ideal part of the human body for biometric identification for several reasons:

  • It is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane (the cornea). This distinguishes it from fingerprints, which can be difficult to recognize after years of certain types of manual labor.
  • The iris is mostly flat, and its geometric configuration is only controlled by two complementary muscles (the sphincter pupillae and dilator pupillae) that control the diameter of the pupil. This makes the iris shape far more predictable than, for instance, that of the face.
  • The iris has a fine texture that—like fingerprints—is determined randomly during embryonic gestation. Even genetically identical individuals have completely independent iris textures, whereas DNA (genetic "fingerprinting") is not unique for the about 0.2% of the human population who have a genetically identical twin.
  • An iris scan is similar to taking a photograph and can be performed from about 10 cm to a few meters away. There is no need for the person to be identified to touch any equipment that has recently been touched by a stranger, thereby eliminating an objection that has been raised in some cultures against fingerprint scanners, where a finger has to touch a surface, or retinal scanning, where the eye can be brought very close to a lens (like looking into a microscope lens).
  • Some argue that a focused digital photograph with an iris diameter of about 200 pixels contains much more long-term stable information than a fingerprint.
  • The originally commercially deployed iris-recognition algorithm, John Daugman's IrisCode, has an unprecedented false match rate (better than 10−11).
  • While there are some medical and surgical procedures that can affect the color and overall shape of the iris, the fine texture remains remarkably stable over many decades. Some iris identifications have succeeded over a period of about 30 years.


  • Iris scanning is a relatively new technology and is incompatible with the very substantial investment that the law enforcement and immigration authorities of some countries have already made into fingerprint recognition.
  • Iris recognition is very difficult to perform at a distance larger than a few meters and if the person to be identified is not cooperating by holding the head still and looking into the camera. However, several academic institutions and biometric vendors are developing products that claim to be able to identify subjects at distances of up to 10 meters ("standoff iris" or "iris at a distance").
  • As with other photographic biometric technologies, iris recognition is susceptible to poor image quality, with associated failure to enroll rates.
  • As with other identification infrastructure (national residents databases, ID cards, etc.), civil rights activists have voiced concerns that iris-recognition technology might help governments to track individuals beyond their will.
  • Irides are not typically deposited by perpetrators at crime scenes, and so are not as useful as fingerprints and DNA for forensic identification.
  • High Definition photographs can be used to fool an iris scanning system. There's no live tissue scanning.


1. To study various iris detection algorithms.

2. To develop a user friendly system that detects and matches different iris patterns.

Literature Survey

Many algorithms have been developed for various steps in iris recognition. Some of which we went through are briefly discussed.

1. Segmentation

A technique is required to isolate and exclude the artefacts as well as locating the circular iris region.

Hough Transform

The Hough transform is a standard computer vision algorithm that can be used to determine the parameters of simple geometric objects, such as lines and circles, present in an image. The circular Hough transform can be employed to deduce the radius and centre coordinates of the pupil and iris regions.

There are a number of problems with the Hough transform method. First of all, it requires threshold values to be chosen for edge detection, and this may result in critical edge points being removed, resulting in failure to detect circles/arcs. Secondly, the Hough transform is computationally intensive due to its ‘brute-force' approach, and thus may not be suitable for real time applications.

Daugman's Integro-Differential Operator

Daugman makes use of an integro-differential operator for locating the circular iris and pupil regions, and also the arcs of the upper and lower eyelids. It works with raw derivative information, so it does not suffer from the thresholding problems of the Hough transform. However, the algorithm can fail where there is noise in the eye image, such as from reflections, since it works only on a local scale.

2. Normalization

The normalization process will produce iris regions, which have the same constant dimensions, so that two photographs of the same iris under different conditions will have characteristic features at the same spatial location.

Daugman's Rubber Sheert Model

The homogenous rubber sheet model devised by Daugman remaps each point within the iris region to a pair of polar coordinates (r,θ) where r is on the interval [0,1] and θ is angle [0,2π].

Even though the homogenous rubber sheet model accounts for pupil dilation, imaging distance and non-concentric pupil displacement, it does not compensate for rotational inconsistencies. In the Daugman system, rotation isaccounted for during matching by shifting the iris templates in the θ direction until two iris templates are aligned.

3. Feature Encoding

The significant features of the iris must be encoded so that comparisons between templates can be made. Most iris recognition systems make use of a band pass decomposition of the iris image to create a biometric template.

Gabor Filters

Gabor filters are able to provide optimum conjoint representation of a signal in space and spatial frequency. A Gabor filter is constructed by modulating a sine/cosine wave with a Gaussian. This is able to provide the optimum conjoint localisation in both space and frequency, since a sine wave is perfectly localised in frequency, but not localised in space. Daugman makes uses of a 2D version of Gabor filters in order to encode iris pattern data.

Log-Gabor Filters

A disadvantage of the Gabor filter is that the even symmetric filter will have a DC component whenever the bandwidth is larger than one octave. However, zero DC components can be obtained for any bandwidth by using a Gabor filter which is Gaussian on a logarithmic scale; this is known as the Log-Gabor filter.

4. Matching Algorithms

Hamming Distance

The Hamming distance gives a measure of how many bits are the same between two bit patterns. Using the Hamming distance of two bit patterns, a decision can be made as to whether the two patterns were generatedfrom different irides or from the same one.


Major Steps of any biometrics, including Iris scan, are discussed below:-

Image Acquisition

It is the process of acquiring high definition iris images either from iris scanner or pre-collected images. These images should clearly show the entire eye especially iris and pupil part.


In automated analysis of digital images, a subproblem often arises of detecting simple shapes, such as straight lines, circles or ellipses. In many cases an edge detector can be used as a pre-processing stage to obtain image points or image pixels that are on the desired curve in the image space. This is achieved using Hough Transform.

Preprocessing includes Iris isolation, an iris-recognition algorithm first has to identify the approximately concentric circular outer boundaries of the iris and the pupil in a photo of an eye. To locate the pupil and iris Daugman Algoithm uses an integro differential operator that acts as a circular edge detector, blurred at a scale set by theta, searching through three parameter space defined by x, y, and r. Where x and y are the center coordinates and r is the radius. A similar method is used to find the upper and lower eyelids. If 50% of the iris between the upper and lower eyelids is not visible, the iris is rejected.

Feature Extraction

The set of pixels covering only the iris is then transformed into a bit pattern that preserves the information. To encode the iris, quadrature 2-D Gabor wavelets are used which produce 2048 phase-bits. Algorithm also computes another 2048 of masking bits which he uses to determine if there are any eyelid occlusions, specular reflections, etc. Even a very poorly focused iris can be demodulated (encoded).

Matching Of An Image

To authenticate via identification (one-to-many template matching) or verification (one-to-one template matching), a template created by imaging the iris is compared to a stored value template in a database. If the Hamming distance is below the decision threshold, a positive identification has effectively been made e.g. a hamming distance of 0 would result in a perfect match. The test of statistical independence is implemented using a Boolean XOR and then a hamming distance is computed to determine how similar two irides are;A 300 MHz machine can search through 100,000 irises a second. An iris has about 3.2 b/mm^2.

Application And Scope

Some areas of application are as follows:

1. Airport

This system can be used in keeping the record of people arriving and departing the airport. Also it can be implemented to give access to restricted areas of the airport to required personnel only based on the employee's iris pattern.

2. Immigration Office

This system can be used to keep record of the tourists visiting the country and can be used to replace the traditional passport control for frequent business travels.

3. Banks/Organization

This system can be used for authentication of employees, staff and the clients of the bank/organization for entrance and/or transaction for clients. Also it can be used for lockers where only the subscriber of the locker can be identified and granted access and for providing access to restricted areas of the banks and organizations to qualified or privileged employees only.

4. Crime Investigation

This system can be used to verify criminals, for which the iris code of the criminals should exist in the database.

5. Computer Access

This system can be implemented in computers for secure and/or legitimate login by replacing the traditional user login system.

Project Schedules


[1] "How Iris Recognition Works". [Online]. Available: "".

[2] "John Daugman How Iris Recognition Works Review [Online]. Available: "". [2006]

[3] "Iris Recognition". [Online] Available: "".

[4] "Recognition of Human Iris Patterns for Biometric Identification", Libor Masek. [PDF]

[5] "Daugman's Iris Scanning Algorithm", W. A. Barrett [PDF]