SIS Iris Recognition Technology

Introduction

Identity; It is the definition of who we are. Even today, identities are being confirmed by two methods:

1. What we have
2. What we know

What we have is comprised of cards or documents such as CNICs (computerized national identity cards), driver licenses, passports or bay-form / birth certificates.

What we know typically consists of P/Ws (passwords) or PINs (personal identification numbers).

But as documents get counterfeited, passports are stolen and passwords are forgotten, we can no longer recognize/verify/authenticate an individual’s identity with any accuracy. And as a result, security of our restricted areas such as air/sea ports, buildings, finances and more is compromised.

To solve this challenge, identities are more frequently confirmed by a third party/component: who we are. We measure who we are in terms of biometrics, digital representations of our faces, finger-patterns, irises, retinas, palms, voice patterns, signatures – the list goes on.

Today, biometrics are the most accurate and secure representation of whom we truly are and can best keep others from stealing, misrepresenting or creating false identities.

SIS Iris Recognition Technology

Soft Innoavtive Solutions (SIS) Iris recognition technology is based on the distinctly colored ring surrounding the pupil of the eye. Made from elastic connective tissue, the iris is a very rich source of biometric data, having approximately 266 distinctive characteristics. These include the trabicular meshwork, a tissue that gives the appearance of dividing the iris radially, with striations, rings, furrows, a corona, and freckles. Iris recognition technology uses about 173 of these distinctive characteristics. Formed during the 8 months of gestation, these characteristics reportedly remain stable throughout a human’s lifetime, except in cases of injury. Iris recognition can be used in verification, identification and authentication systems. Iris recognition systems use a small, high-quality camera to capture a high-resolution image of the iris. The systems then define the boundaries of the iris, establish a coordinate system over the iris, and define the zones for analysis within the coordinate system.

Iris patterns become interesting as an alternative approach to reliable visual recognition of humans when imaging can be done at distances of less than a meter, and especially when there is a need to search very large databases without incurring any false matches despite a huge number of possibilities. Although small (11 mm) and sometimes problematic to image, the iris has the great mathematical advantage that its pattern variability among different persons is enormous. In addition, as an internal (yet externally visible) organ of the eye, the iris is well protected from the environment and stable over time. As a planar object its image is relatively insensitive to angle of illumination, and changes in viewing angle cause only affine transformations; even the non affine pattern distortion caused by pupillary dilation is readily reversible. Finally, the ease of localizing eyes in faces, and the distinctive annular shape of the iris, facilitates reliable and precise isolation of this feature and the creation of a size-invariant representation.

SIS Iris Recognition KB-101

Model: KB-101
Material: Plastic
Colour: Black

The KB-101 is the 100% Proof of SIS Iris Recognition Technology that has gone through all the phases of (controlled environment) testing and has been deployed for a series of (uncontrolled environment) field tests.

So far there has been no single error recorded (FMR, FNMR, FTER) in KB-101 but the field test will last till the end of 2009.

KB-101 Product Features:

• User friendly /Easy to use.
• Hands-free operation.
• Recognition time: Less than 2sec
• Recognition process time: Less than 10sec
• Operating range: 300mm - 400mm
• No additional maintenance fee needed after intial installation
• Ability to handle very large populations at high speed.
• Convenient: all a person needs to do is look into a camera for few seconds (can adjust himself/herself with the help of display screen).
• An image is taken which is non-invasive and inherently safe.
• The iris itself is stable throughout a person’s life (approximately from the age of one); the physical characteristics of the iris don't change with age.

SIS Iris Recognition KS-010

Model: KS-010
Material: Metal
Colour: Silver


The KS-010 is another breakthrough achievement by
Soft Innovative Solution's Technical & Designing Team.

SIS Iris Recognition WM-100

Model: WM-100
Material: Plastic
Colour: Black

The WM-100 is a marvelous achievement by

Soft Innovative Solution's Technical & Designing Team.

The exquisite use of industrial plastic reduced the

fragility and weight issue.

SIS Iris Recognition Device WM Prototype

Model Type: Wall Mounted
This was Soft Innovative Solutions's (SIS) first Iris Recognition Device prototype that we designed on which controlled testing was conducted.



Drawbacks: Fragility & Heavy in weight.

Accuracy of Biometric Techniques

It is important to understand fully the accuracy of biometric devices before deploying it. Biometric like other technologies is not 100% accurate but it enhances security level and makes it difficult to breach the security.

The three key performance metrics are
1. False match rate (FMR)
2. False non match rate (FNMR)
3. Failure to enroll rate (FTER).

False Match Rate (FMR)
A false match occurs when a system incorrectly matches an identity, and FMR is the probability of individuals being wrongly matched. In verification and positive identification systems, unauthorized people can be granted access to facilities or resources as the result of incorrect matches. In a negative identification system, the result of a false match may be to deny access. For example, if a new applicant to a public benefits program is falsely matched with a person previously enrolled in that program under another identity, the applicant may be denied access to benefits.

False Non Match Rate (FNMR)
A false non match occurs when a system rejects a valid identity, and FNMR is the probability of valid individuals being wrongly not matched. In verification and positive identification systems, people can be denied access to some facility or resource as the result of a system’s failure to make a correct match. In negative identification systems, the result of a false non match may be that a person is granted access to resources to which she should be denied. For example, if a person who has enrolled in a public benefits program under another identity is not correctly matched, she will succeed in gaining fraudulent access to benefits.

Failure to Enroll Rate (FTER)
FTER is a biometric system’s third critical accuracy metric. FTER measures the probability that a person will be unable to enroll. Failure to enroll (FTE) may stem from an insufficiently distinctive biometric sample or from a system design that makes it difficult to provide consistent biometric data. The fingerprints of people who work extensively at manual labor are often too worn to be captured. A high percentage of people are unable to enroll in retina recognition systems because of the precision such systems require. People who are mute cannot use voice systems, and people lacking fingers or hands from congenital disease, surgery, or injury cannot use fingerprint or hand geometry systems. Although between 1 and 3 percent of the general public does not have the body part required for using any one biometric system, they are normally not counted in a system’s FTER.

Strength & Weaknesses of different BTs

Various biometric technologies have been described. There is no single biometric technique that outperforms all of them. Every technique has its own merits and demerits and usage of any technology is context dependent. Below we are giving strengths and weakness of each technology for comparison.

Strengths of Facial Recognition

• Effective for surveillance applications.


• Provides a first level “scan” within an extremely large, low-security situation.


• Easy to deploy, can use standard CCTV hardware integrated with face recognition software.


• Passive technology does not require user cooperation and works from a distance.


• May be able to use high quality images in an existing database.

Weaknesses of Facial Recognition

• Lighting, age, glasses, and head/face coverings all impact false reject rates.


• Even in surveillance applications, lower accuracy results in multiple candidates return in large populations. As a result, secondary processing is required for surveillance operations.


• Privacy concerns: people do not always know when their picture/image is being taken and being searched in a database — or worse, being enrolled in a database. Can be used without explicit opt-in permission

Strengths of Fingerprints

• Widely accepted by civil law enforcement and forensic government applications (the AFIS database); as such, fingerprints are excellent for background checks.


• Can provide a relatively low false rejection rate and false acceptance rate when used in populations with a low incidence of “outliers” (however, large groups or groups of varied by race and gender are an issue).


• Wide range of vendors and solutions.


• Ability to enroll multiple fingers.

Weaknesses of Fingerprints

• Fingerprint is not as accurate as iris recognition


• Fingerprint false accept rate varies by vendor, and is approximately 1 in 100,000.


• Iris recognition false accepts rate is 1 in 1.2 million statistically.


• Most high-end fingerprint systems measure approximately 40-60 characteristics; iris recognition looks at about 240 characteristics.

Strengths of Hand Geometry

• Currently being used for functions such as access control, employee time recording and point of sale applications.


• Fairly easy to use.


• Reasonably high acceptance among users and it is opt-in.


• Works in challenging environments.

Weaknesses of Hand Geometry

• Does not support 1: all matching with large databases.


• Weather, temperature and medical conditions such as pregnancy or certain medications can affect hand size.


• Hand size and geometry changes over time, especially in the very young and the very old.


• People are reluctant to place hand where many others have touched (hygiene issue).


• Extreme sizes are not accommodated in all hand readers.


• Fairly expensive and large equipment is required.

Strengths of Iris Recognition

• Hands-free operation.


• Proven highest accuracy: iris recognition had no false matches in over two million cross-comparisons, according to Biometric Product Testing Final Report


• Ability to handle very large populations at high speed: Iris recognition can handle very large 1: all searches within extremely large databases


• Convenient: all a person needs to do is look into a camera for a few seconds.


• A video image is taken which is non-invasive and inherently safe.


• The iris itself is stable throughout a person’s life (approximately from the age of one); the physical characteristics of the iris don't change with age.

Weaknesses of Iris Recognition

• Not a very user friendly.


• Can not recognize the person from the crowd like facial system.