Biometrics: Eyes Don't Lie

Privacy of personal data is an illusion in today's complex society. With

only passwords, or Social Security Numbers as identity or security measures

every one is vulnerable to invasion of privacy or breach of security.

Traditional means of identification are easily compromised and anyone can use

this information to assume another's identity.

Sensitive personal and corporate information can be assessed and even

criminal activities can be performed using another name.

India's civil aviation ministry is planning high tech methods such as

biometric identification and iris testing at airports, as part of identity

authentication measures. The ministry believes these measures would be more

accurate and less time-consuming.

Eye pattern recognition system provides a barrier to and virtually eliminates

fraudulent authentication and identity privacy and safety controls privileged

access or authorized entry to sensitive sites, data or material. In addition to

privacy protection, there are myriad of applications were iris recognition

technology can provide protection and security. This technology offers the

potential to unlock major business opportunities by providing high confidence

customer validation.

Of all the available biometric techniques, fingerprint identification has

been used the most. Common applications for fingerprint identification include

access control (buildings, ATMs, and computers are examples), forensics, and

driver-license registration. Systems using fingerprint ID systems usually employ

fast processing engines (RISC or DSP) and often, large databases (the FBI's

database contains over 70 mn fingerprints). To prevent someone from using a

fingerprint on a passive surface, such as a piece of sticky tape, newer systems

employ criteria beyond fingerprint matching.

Organizations use voice recognition for access to private areas such as

company intranets, computer networks, and financial databases. Unlike a

fingerprint ID system, which needs a special sensor, voice-recognition equipment

uses standard electronics components. A drawback of voice-recognition systems is

that they are of limited use in a noisy environment.

Pattern recognition is a widely used term for all kind of processes where

specific information is extracted from a large set of individual measurements.

Facial Recognition System



A facial recognition system is a computer-driven application for

automatically identifying a person from a digital image. It does that by

comparing selected facial features in the live image and a facial database.

Among the popular recognition algorithms are eigenface, fisherface, the

Hidden Markov model, and the neuronal motivated Dynamic Link Matching. A newly

emerging trend, claimed to achieve previously unseen accuracies, is

three-dimensional face recognition.

In order to verify the identity of an individual, a facial recognition system

goes through various steps. The first step involves capturing an image of the

individual's face with the use of a video camera. An algorithm called local

feature analysis (LFA) is then used to match distinctive features of the

individual's face to images contained in the system

These distinctive features are called nodal points. There are approximately

80 nodal points on the human face, such as the distance between the eyes, the

shape of jaw line, and the shape of the chin.

Generally, facial recognition systems account for slight variations in these

nodal points that may occur due to changes in facial expressions. After scanning

a database of images and finding a match, the facial recognition system rates

the possibility of the correct match, relying on the information it has

processed. Since many systems have the ability to recognize an individual's

face within seconds, a correct match may be determined while the individual is

still within a specific area.

In 2003, Identix Inc was named as one of the most reliable facial recognition

software companies. Since then, the company has further developed its product,

FaceIt®ARGUS. FaceIt® can provide face finding, face recognition, liveness and

image quality services.

Many facial recognition systems take advantage of Local Feature Analysis (LFA);

however, FaceIt is the only technology to utilize both LFA and skin biometrics,

uniqueness of skin texture.

Application Galore



Face recognition has a wide range of applications such as face-based video

indexing and browsing engines, multimedia management, human-computer

interaction, biometric identity authentication, and surveillance. Interest and

research activities in face recognition have increased significantly in the past

years. This is partly due to recent technology advances initially made by work

on eigenfaces and partly due to increased concerns in security. However, the

problem of face recognition remains a great challenge after several decades of

research.

Problems in Face Recognition



It is difficult for conventional methods to achieve high accuracy. The

appearance of a face is affected by extrinsic factors, including illumination,

pose and expression, as well as inaccuracies made in pre-processing stages such

as face alignment. Variations brought about by extrinsic factors make individual

face manifolds highly complex.

Speed is also an important issue for many applications. To achieve high

accuracy, the recognition should be performed based on intrinsic properties, and

the algorithms should be able to deal with unfavorable influences due to

extrinsic factors and mis-alignment.

Iris Recognition



The iris is the pigmented tissue in the eyeball that surrounds the pupil,

and consists of the muscles that adjust the size of the pupil. This pattern is

unique to each individual, is fixed about two years after birth, and remains

unchanged for the rest of one's life.

Iris recognition is a promising approach for confirming identity on a network

In contrast to systems up to now that used ID cards and passwords, systems that

use iris recognition reference each individual's unique iris code, so it is

possible to maintain an extremely high level of security. Since it is also

possible to basically check one's identity without making contact, there are

no unpleasant feelings like there are when giving fingerprints.

Two types of eye scanning technologies are in use-retinal and iris. No

other biometric offers as much information as our eyes. The potential of

eye-scan identification has been known for decades. Both the iris and the retina

are distinctive, and remain stable over time. Retinal scanning was first

conceived in the 1970s, but the invasiveness and costs associated with imaging

the retina prohibited widespread market acceptance. The concept of iris

recognition was patented in 1986, and the software algorithms to realize the

technology emerged in the mid nineties. Iris recognition is less invasive, less

expensive, and highly accurate.

How It Works: 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. The set of pixels

covering only the iris is then transformed into a bit pattern that preserves the

information that is essential for a statistically meaningful comparison between

two iris images.

The mathematical methods used resemble those of modern lossy compression

algorithms for photographic images. In the case of general

IrisCode, a Garbor wavelet transform is used in order to extract the

spatial frequency range that contains a good best signal-to-noise ratio

considering the focus quality of available cameras. The results are a set of

complex numbers that carry local amplitude and phase information for the iris

image. In IrisCode, all amplitude information is discarded, and the resulting

2,048 bits that represent an iris consist only of the complex sign bits of the

representation of the iris image. Discarding the amplitude information ensures

that the IrisCode remains largely unaffected by changes in illumination and iris

color, which contributes significantly to the long-term stability of the code.

To verify an IrisCode, its Hamming distance to a previously recorded IrisCode

has to be below a suitable selected threshold.

The Challenges: A practical

problem of iris recognition is that the iris is usually partially covered by

eyelids and eyelashes. In order to reduce the false-reject risk in such cases,

additional algorithms are needed to identify the locations of eye lids and eye

lashes, and exclude the bits in the resulting code from the comparison

operation.

Like with most other biometric identification technology, a still not

satisfactorily solved problem with iris recognition is the problem of “live

tissue verification”. The reliability of any biometric identification depends

on ensuring that the signal acquired and compared has actually been recorded

from a live body part of the person to be identified, and is not a manufactured

template. Many commercially available iris recognition systems are easily fooled

by presenting a high-quality photograph of a face instead of a real face, which

makes such devices unsuitable for unsupervised applications, such as door

access-control systems. The problems of live tissue verification is less of a

concern in supervised applications (eg immigration control), where a human

operator supervises the process of taking the picture.

The Road Ahead



Biometric technologies are becoming a part of everyday life as an innovative

way to control access to buildings and information. Researchers are developing

devices that will recognize a person's smell, walk and even DNA.

Iris-recognition technology is one of the more popular options because of its

accuracy and convenience. LG Electronics has installed the iris devices in

banks, airports and other high-security areas. Last summer, the Nine Zero hotel

in Boston became the first hotel to use it. By 2008, sales of biometric

technologies are expected to reach $4.6 bn, up from $1 bn last year. Without a

doubt, they will become a part of everyday life in the near future.

Amardeep Gupta





maildqindia@cybermedia.co.in



The author is head, Department of Computer Science, DAV College, Amritsar