PalmPrint ApplicationBY:Arjit AgrawalAbhishek PatelAshish Gupta

CONTENTSCONTENTSBiometric?Introduction: Palm PrintHistoryDefinitionFeaturesApproachDevelopmentsAdvantages & ApplicationStandardsConclusion

Biometrics?Biometrics?There is an ever

growing need to identify individuals.

BestBest securitysecurity solution:solution: Biometrics.Biometrics.

Following are Following are types Fingerprint, types Fingerprint, PalmPrint, Iris, PalmPrint, Iris, Face Recognition,Face Recognition, Voice Authentication

IntroductionIntroduction

Palm print recognition inherently implements many of the same matching characteristics that have allowed fingerprint recognition to be one of the most well-known and best publicized biometrics. Both palm and finger biometrics are represented by the information presented in a friction ridge impression.

Because fingerprints and palms have both uniqueness and permanence, they have been used for over a century as a trusted form of identification .

Palmprint Past ApplicationsPalmprint Past Applications

Palmprint explanation from Chinese Hand Book for fortune telling had been exited over a few thousand years.

Some policemen have used palmprints in their inspection for a long time since they are stable physical characteristics

HistoryHistory

In many instances throughout history, examination of handprints was the only method of distinguishing one illiterate person from another since they could not write their own names.

The first known AFIS system built to support palm prints is believed to have been built by a Hungarian company.

Australia currently houses the largest repository of palm prints in the world. The new Australian National Automated Fingerprint Identification System (NAFIS) includes 4.8 million palm prints.

Definitions of Palmprint Palm: The inside part

of our hand from the wrist to the end of our fingers.

Palmprint: The skin patterns of a palm, composed of the physical characteristics of the skin patterns of a palm, such as lines, points, and texture.

Palmprint authentication: The way of personal authentication using unique palmprint features, either human observable or not.

Palmprint Features

Geometry features: Finger length, width, thickness and area of a palm

Texture/Line Features: Principal lines,Wrinkles

Point Features: Minutiae point, Delta point, Datum point

Outlay

Approach Concept:Palm identification, just like fingerprint

identification, is based on the aggregate of information presented in a friction ridge impression.

Palm print appears as a series of dark lines and represents the high, peaking portion of the friction ridged skin while the valley between these ridges appears as a white space and is the low, shallow portion of the friction ridged skin

HardwareA variety of sensor types CapacitiveOpticalUltrasoundThermal

TechniquesThe three main categories of

palm matching techniques are minutiae-based matching, correlation-based matching,ridge-based matching.

Working

Development

2D Palmprint System: Prototype

AdvantagesMore distinctive than fingerprint.Much cheaper than iris devices.Features can be extracted from

low resolution images.More reliable and highly

accurate.

Comparison

ApplicationsAccess control / Time

& AttendanceGovernment /

Commercial Identity Management Systems

User Authentication or Server Systems

OEM Terminal Devices (POS, ATMs or Information Kiosks)

Other Industry-Specific Applications

United States Government Evaluations Unlike several other biometrics, a

large-scale Government-sponsored evaluation has not been performed for palm recognition.

The FBI Laboratory is currently encoding its hard-copy palm records into three of the most popular commercial palm recognition systems.

Standards Overview Major standards efforts for palm prints

currently underway are the revision to the ANSI NIST ITL-2000 Type-15 record. Many, if not all, commercial palm AFIS systems comply with the ANSI NIST ITL-2000 Type-15 record for storing palm print data. Several recommendations to enhance the record type are currently being “vetted” through workshops facilitated by the National Institute for Standards and Technology.

SummaryEven though total error rates are

decreasing when comparing live scan enrolment data with live-scan verification data, improvements in matches between live-scan and latent print data are still needed.

But there are still significant challenges in balancing accuracy with system cost.

Future challenges require balancing the need for more processing power with more improvements in algorithm technology to produce systems that are affordable to all levels of law enforcement.

SamplesSamples

REFERENCESREFERENCEShttp://

www.biometricscatalog.org/NSTCSubcommittee

C H Chen, P S P Wang, Handbook of Pattern Recognition and Computer Vision.

Prof. David Zhang, Palmprint Identification, Chair Professor/ Department Head The Hong Kong Polytechnic University

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