Pattern Recognition

Pattern Recognition

1/6/2009

Instructor:

Wen-Hung Liao, Ph.D.

Biometrics

Outline

Basic Concepts Fingerprint Iris Scan Hand Geometry Face Recognition

Identification vs Verification

Identification: Who am I? One-to-many search

Verification: Am I who I claim I am? One-to-one search

Detection: Find out whether there is an instance of a given type of object in an environment.

Recognition: detection + identification

Terminology False Acceptance Rate (FAR) : the probability that a

biometric device will allow a ‘bad guy’ to pass. Related to security.

False Rejection Rate (FRR):the probability that a biometric device won't recognize a good guy. Related to convenience.

The point where false accept and false reject curves cross is called the "Equal Error Rate." The Equal Error Rate provides a good indicator of the unit's performance. The smaller the Equal Error Rate, the better.

Validity of Test Data

Testing biometrics is difficult, because of the extremely low error rates involved.

Some are based on theoretical models. Some are obtained from actual field testing. It's important to remember that error rates

are statistical: they are derived from a series of transactions by a population of users.

What is a good biometric feature?

Uniqueness Invariance Non-intrusive Easy (or not too difficult) to acquire Low processing cost

Fingerprint

Finger-scan biometrics is based on the distinctive characteristics of the human fingerprint.

A fingerprint image is read from a capture device, features are extracted from the image, and a template is created.

If appropriate precautions are followed, what results is a very accurate means of authentication.

Fingerprints vs Finger-scans Fingerprint images require 250kb per finger

for a high-quality image. Can be acquired using ink-and-roll

procedure, optical or non-contact methods. Finger-scan technology doesn't store the

full fingerprint image. It stores particular data about the fingerprint in a much smaller template, requiring from 250-1000 bytes.

AFIS

AFIS (Automated Fingerprint Identification Systems) - commonly referred to as "AFIS Systems" (a redundancy) - is a term applied to large-scale, one-to-many searches.

Although finger-scan technology can be used in AFIS on 100,000 person databases, it is much more frequently used for one-to-one verification within 1-3 seconds.

Fingerprint Characteristics

Can be classified according to the decades-old Henry system: left loop right loop arch whorl tented arch

Feature Extraction Steps

Minutiae, the discontinuities that interrupt the otherwise smooth flow of ridges, are the basis for most finger-scan authentication.

Accuracy False Rejection Rates (FRR), or the likelihood

that the system will not "recognize" an enrolled user's finger-scan, in the vicinity of 0.01%.

False Acceptance Rates (FAR), or the likelihood that the system will mistakenly "recognize" the finger-scan of a user who is not in the system, are frequently stated in the vicinity of 0.001%.

The point at which the FAR and FRR meet is the Equal Error Rate, frequently claimed to be 0.1%.

Iris Scan

Iris recognition is based on visible (via regular and/or infrared light) qualities of the iris.

A primary visible characteristic is the trabecular meshwork (permanently formed by the 8th month of gestation), a tissue which gives the appearance of dividing the iris in a radial fashion.

Other visible characteristics include rings, furrows, freckles, and the corona.

Iris Recognition Technology

Iris recognition technology converts the visible characteristics discussed before into a 512 byte IrisCode(tm), a template stored for future verification attempts.

Accuracy

The odds of two different irises returning a 75% match (i.e. having a Hamming Distance of 0.25): 1 in 10^16

Equal Error Rate (the point at which the likelihood of a false accept and false reject are the same): 1 in 1.2 million

The odds of 2 different irises returning identical IrisCodes: 1 in 10^52

Benefits

Uniqueness Established prior to birth and

remains intact through out the life.

For more details

Check Dr. John Daugman’s web page:

http://www.cl.cam.ac.uk/users/jgd1000

Hand Scan Hand-scan reads the top and sides of the

hands and fingers, using such metrics as the height of the fingers, distance between joints, and shape of the knuckles.

Although not the most accurate physiological biometric, hand scan has proven to be an ideal solution for low- to mid-security applications where deterrence and convenience are as much a consideration as security and accuracy.

Example HandPunch

2000/3000 model developed by Recognition Systems

Pros and Cons

Advantages Ease of use Resistant to

fraud Template size User perception

Disadvantages Static design Cost Injury to hands Accuracy

Face Recognition

Most natural because this is how we human recognize other people.

Remains a difficult subject.

Primary Facial Scan Technologies Eigenfaces feature analysis neural network automatic face processing

Typical Eigenfaces

Feature Analysis The most widely utilized facial recognition

technology Local Feature Analysis (LFA) utilizes

dozens of features from different regions of the face, and also incorporates the relative location of these features.

The extracted (very small) features are building blocks, and both the type of blocks and their arrangement are used to identify/verify.

ANN Approach Features from both faces - the

enrollment and verification face - vote on whether there is a match.

Neural networks employ an algorithm to determine the similarity of the unique global features of live versus enrolled or reference faces, using as much of the facial image as possible.

AFP

Automatic Face Processing (AFP) is a more rudimentary technology, using distances and distance ratios between easily acquired features such as eyes, end of nose, and corners of mouth.

Not as robust, but AFP may be more effective in dimly lit, frontal image capture situations.