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IRIS Detection or IRIS Recognition

Written By: 

Preeti Jain
Day in and day out we need to prove our identity at various places, for secure access to buildings, air and rail travel, to cast a vote, etc. Security and the authentication of individuals are necessary in different spheres of life.
Traditionally, we have been using photo-based identity cards to prove our identity and we have been quite accustomed to it. These identity cards were augmented/replaced by passwords in many cases. However, of late, these conventional identity proofs are being replaced by biometric identifications to overcome many of the shortfalls of these methods. Principle of COB Technology
Fig. 1: A Representational Image of IRIS Recognition
One of the most dangerous security threats is the impersonation, in which somebody claims to be somebody else. Trend in the identification & authentication world is headed towards biometrics. Finger prints, face recognition, Iris Recognition, DNA pattern recognition are replacing printed identity cards. Biometric identification provides a valid alternative to conventional authentication mechanisms such as ID cards and passwords. Biometric identification identifies an individual based on “who they are (Biometrics)” rather than “what they possess (passport)” or “what they remember (password)”.
Amongst various biometric authentication systems, IRIS recognition system is very important technique that uses pattern-recognition techniques on images of irides to uniquely identify an individual. Iris recognition is a particular type of biometric system that can be used to reliably identify a person by analyzing the patterns found in the iris. Because of the uniqueness of the iris, this is a very reliable form of authentication system. Although there is a genetic influence, particularly on the iris’ colour, the iris develops through folding of the tissue membrane and then degeneration (to create the pupil opening) which results in a random and unique iris.
The fractal structure of the iris is unique for each eye of each person, even among twins. Authentication systems based on Iris detection and verification offer unique advantages
·         There is huge variability of the pattern between irides of different individuals. Due to this, large databases can be searched without finding any false matches.
·         Identity cards just confirm their given identity whereas irides can be used to identify individuals. Irides show that an individual is not who they say they are but also show exactly who they are.
·         Iris has genetic independence
·         Iris recognition is rarely impeded by glasses or contact lenses and can be scanned from 10 cm to a few meters away.  
·         The iris remains stable over time as long as there are no injuries and a single enrolment scan can last a lifetime
·         Even blind people can use this scan technology since iris recognition technology is iris pattern-dependent not sight dependent.
·         Iris detection is a non evasive method. The imaging process involves no lasers or bright lights and authentication is essentially non-contact. Today’s commercial iris cameras use infrared light to illuminate the iris without causing harm or discomfort to the subject.
·         Iris cameras, recreates an encrypted digital template of Iris pattern.  This encrypted template cannot be re-engineered or reproduced in any sort of visual image and therefore protects identity theft.

Know Your IRIS

The human Iris is an internal organ of the eye, protected by the eyelid, cornea and the aqueous humour. The iris is a thin circular diaphragm, which lies between the cornea and the lens of the human eye. The iris is perforated close to its centre by a circular aperture known as the pupil. The function of the iris is to control the amount of light entering through the pupil, and this is done by the sphincter and the dilator muscles, which adjust the size of the pupil. The average diameter of the iris is 12 mm, and the pupil size can vary from 10% to 80% of the iris diameter.
Iris is part of the middle coat of the eye and lies in front of the lens. It is the only internal organ of the body that is normally visible externally. Iris is considered the most unique and data rich physical structure on the human body. One of the key characteristics of iris is that the iris features remain constant throughout the years. The iris is composed from several layers. The lowest layer, epithelium layer, contains dense pigmentation cells. Next layer, stromal layer, contains blood vessels, pigment cells and the two iris muscles. The density of stromal pigmentation determines the colour of the iris. Among the visible features of an multi-layered iris are two zones, which often differ in colour. An outer ciliary zone and an inner pupillary zone, and these two zones are divided by the collarette – which appears as a zigzag pattern.
 Principle of COB Technology
Fig. 2: An Image Showing Iris of the Eye Along with other Components of an Eye
Because every iris is unique and it remains unchanged in clinical photographs. It was proposed to use the iris of the eye as a kind of optical fingerprint for personal identification. It works even when people wear sunglasses or contact lenses.
Some of the properties of the iris that enhance its suitability for use in automatic identification are:
·         Immunity from the external environment
·         Impossibility of surgically modifying without the risk of vision
·         Physiological response to light
·         Ease of registering its image at some distance
Images of the iris adequate for personal identification with very high confidence can be acquired from distances of up to about 3 feet (1 meter). The striated anterior layer covering the trabecular meshwork creates the predominant texture seen with visible light, but all of these sources of radial and angular variation taken together constitute a distinctive "fingerprint" that can be imaged from some distance.

IRIS & Recognition Steps

Iris recognition relies on the unique patterns of the human iris to identify or verify the identity of an individual. For iris recognition, an input video stream is taken using Infra-red sensitive CCD camera and the frame grabber. From this video stream eye is localized using various image processing algorithms. Area of interest i.e. iris is then detected from the eye and the features are extracted. These features are encoded into pattern which is stored in the database for enrollment and are matched with the database for authentication.
To achieve automated iris recognition, following are the main steps:
Principle of COB Technology
Fig. 3: A Block Diagram Representing Steps to Achieve Automated Iris Recognition
1.      Iris Isolation
Locating the iris is not a trivial task since its intensity is close to that of the sclera and is often obscured by eyelashes and eyelids. However the pupil, due to its regular size Principle of COB Technology
Fig. 4: A Representational Image of IRIS Isolation
and uniform dark shade, is relatively easy to locate. The pupil and iris can be approximated as concentric and this provides a reliable entry point for auto detection.
·         Pupil
The first step in iris detection (also known as preprocessing) is to detect the pupil. The pupil’s intensity and location are fairly consistent in most images and so it lends itself well to auto-detection. Usually, the image captured for iris has many undesired parts like eyelids, pupil etc.
In the literature, multiple ways of detecting the pupil have been presented. Some use edge information whereas others utilize thresholding. In simplest form, search for the largest circular black area is made and that area is treated as pupil.
The circular Hough transform, a standard computer vision algorithm, is commonly employed to deduce the radius and centre coordinates of the pupil and iris regions. Some researchers have made use of the parabolic Hough transform to detect the eyelids, approximating the upper and lower eyelids with parabolic arcs.
Daugman’s Integro-differential Operator, Active Contour Models are some of the other algorithms proposed in the literature. Since the problem is trivial, most methods work well enough.
·                     Iris
Once we have the location of the pupil clearly defined, the complexity of locating the iris is somewhat reduced due to the relative concentricity of the pupil and iris. In contrast to pupil detection, eficiently locating the iris is somewhat more complicated due to (i) the obstruction of the iris by the eyelids for most eyes, (ii) its irregular pattern and (iii) because of the relative similarity with the iris boundary and the sclera.
The outer boundaries of iris are detected with the help of center of pupil. The binary image is taken and concentric circles of different radii are drawn with respect to center of pupil. For a particular circle the change in intensity between normal pointing toward center and away from center is measured. The radius having highest change in intensity is considered as outer boundary.
·                     Eyelids
An image of an eye roughly has three intensities, from darkest to lightest: pupil, iris and sclera, and eyelids. Hence a suitable thresholding method is able to separate the eyelids from the rest of the image. Typically, eyelid detection is incorporated into the iris finding algorithm so that pixels on the eyelids are ignored. The locations of eyelid boundaries are estimated on each iteration. This approach has the advantage that no separate algorithm is needed in finding the eyelid.
·                     Normalization
Iris normalization is done in order to make the image independent of the dimensions of the input image.
Once the iris region is successfully segmented from an eye image, the next stage is to transform the iris region so that it has fixed dimensions in order to allow comparisons. The dimensional inconsistencies between eye images are mainly due to the stretching of the iris caused by pupil dilation from varying levels of illumination. Other sources of inconsistency include, varying imaging distance, rotation of the camera, head tilt, and rotation of the eye within the eye socket. 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.
For normalization, Daugman’s Rubber Sheet Model, Image Registration technique (proposed by Wildes et al.), Virtual Circles (proposed by Boles) are employed.

Encoding the IRIS

2.                  Encoding the Iris
After iris is detected, the algorithms are used to encode the iris data. This process extracts features from the normalized iris images and encodes it to generate iris Codes. Principle of COB Technology
Fig. 5: An Image of Algorithms Used to Encode IRIS Data
Methods available in literature for feature extraction and code generation are:
·                     Wavelet encoding
Wavelet filters are applied to the 2D iris region to decompose the data in the Iris region into components that appear at different resolutions. The output is then encoded in order to provide a compact and discriminating representation of the iris pattern.
·                     Gabor Filters(used by Daugman)
Gabor filters are able to provide optimum conjoint representation of a signal in space and spatial frequency. Decomposition of a signal is accomplished using a quadrature pair of Gabor filters.
Daugman demodulates the output of the Gabor filters in order to compress the data. This is done by quantizing the phase information into four levels (represented by two bits), for each possible quadrant in the complex plane to obtain a compact 256-byte template, which allows for efficient storage and comparison of irises.
·                     Log-Gabor Filters
To negate the disadvantage of Gabor filters of having DC component, Gabor filter which is Gaussian on a log scale, known as Log-Gabor filter, is proposed.
·                     Zero Crossings of 1D wavelet(proposed by Boles and Boashash)
It makes use of 1D wavelets for encoding iris pattern data; the reason is that zero-crossings correspond to significant features with the iris region. The mother wavelet is defined as the second derivative of a smoothing function. The zero crossings of dyadic scales of these filters are then used to encode features.
·                     Haar Wavelet(used by Lim et al)
It also uses wavelet transform to extract features from the iris region; Haar wavelet being the mother wavelet.
·                     Laplacian of Guassian Filters (proposed by Wildes et al)
It decomposes the iris region by application of Laplacian of Gaussian filters to the iris region image. The filtered image is represented as a Laplacian pyramid (with four different resolution levels) which is able to compress the data, to generate compact iris template.

Image Verification

3.      Image Verification from the database
After feature extraction, next step is to compare the code of the input Iris image with the code in the database.
Following are the algorithms used for this purpose.
·                     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 generated from different irises or from the same one.
The Hamming distance is the matching metric employed by Daugman.
·                     Weighted Euclidean Distance
The weighting Euclidean distance gives a measure of how similar a collection of values are between two templates. This metric is employed by Zhu et al.
·                     Normalized Correlation
It makes use of normalized correlation between the acquired and database representation for goodness of match. This metric is employed by Wildes et al.

Anti-Spoofing Mechanisms

Iris pattern is considered as the most accurate and stable biometric modality, however, iris recognition system meets faces challenge in anti-counterfeit iris as color contact lens become popular recently. Attackers wearing contact lens with artificial textures printed onto them may try to spoof the system. Other spoofing mechanisms include ‘eye movie’, iris pattern printed on plastic/rubber eye, etc.
Various researchers have worked in this direction to make iris recognition system more robust to anti-spoofing. Daugman proposed to detect printed iris pattern using spurious energy in 2D Fourier spectra. Lee et al. suggested detecting fake iris based on Purkinje image. He et al. used four features (image mean, variance, contrast and angular second moment) to detect fake iris. Detecting iris edge sharpness, classify based on iris texture, etc. are few other methods

Standards & Applications

IRIS Biometrics Standards
Following biometrics standards have evolved to ensure similarity in all iris images in terms of formatting and the contents.


·         International Organization for Standardization/ International Electrotechnical Commission (ISO/IEC) 19794-6:2005



·         The American National Standards Institute/ International Committee for Information Technology Standards (ANSI/INCITS) 379-2004


·         The American National Standards Institute/ The US National Institute for Standards and Technology (NIST) ANSI/NIST-ITL 1-2007 TYPE 17


·         The US Department of Defense Electronic Biometric Transmission (DoD EBTS)


Commercial IRIS Scanner solution providers
Automated iris scanning technology is still quite young. Following are the vendors providing iris scanning solutions.
Principle of COB Technology
Fig. 6: An Image Listing Top Vendors Providing Iris Scanning Solutions
Smartmatic, Human Recognition systems, IrisGuard, MorphoTrak, LG Electronics are some other companies providing solutions in this area.
Typical Applications
·         Substitution for passports.
·         Controlling access to restricted areas at airports;
·         Office/premises access control;
·         Access Database, Computer logins;
·         Screening/ Verification at border crossings/ sensitive areas;
·         Biometrically enabled Identity Cards.



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