Extended Fingerprint 

Palmprint Feature

Austin Hicklin, Noblis

February 2007
Data Format

 “Data Format for the Interchange of 

Fingerprint and Palmprint Features”

 Addendum to ANSI/NIST-ITL 1-2007 Defines a new Type-18 record

 Current status: 

 Proposed draft Will be made public after comments fro

members are incorporated

 Will remain “proposed draft” for period 

evaluation

 Amended draft will be put out for ANSI 
Purpose

 To define a quantifiable, repeat

clear method of characterizing tinformation content of a fingerpfriction ridge image.
Uses

 Uses may include, but are not l

 automated searches of fingerprint o

systems

 information exchange between hum definitions of information content of 

palmprints

Note that the requirements 

between uses!
Potential Benefits

 Improved accuracy for AFIS sea Common interchange format 

 between human examiners Between AFISs

 More complete basis for quantif

similarity of a given comparison

t

b

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require e

val

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ion 

to 

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pot

All 

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benefit
Committee

Includes 45 people from 23 organizationsBehnam Bavarian (Motorola), Vincent Bouatou (SJohn Burt (NEC), Christophe Champod (University Switzerland), Yi Chen (Michigan State), Vladimir D(NIST), Jeri Eaton , Brian Finegold (BAE), Jean-Ch(Sagem Morpho), Mike Garris (NIST), Ed German (FBI-CJIS), Paul Griffin (Identix), Masanori Hara (Hicklin (Noblis), Tom Hopper (FBI-CJIS), Anil Jain Creed Jones (Sagem Morpho), Artour Karaguiozian Peter Komarinski (IAI), Debbie Leben (US Secret S(TBS), Davide Maltoni (University of Bologna), DaBrian Martin (Identix), Mike McCabe (NIST), Glen Morpho), Steve Meagher (FBI-Lab), John Mayer-SDmitry Mikhailov (Jobin Yvon (SPEX)), Elaine NewNoore (WVU), Geppy Parziale , Wade Petroka (KinSheriff's Office), Ann Punter (Cogent), Richa Singh Soltis (DEA), Scott Swann (FBI-CJIS), Elham TabaThuillier (Sagem Morpho), Anne Wang (Cogent), P(NIST), Kasey Wertheim, Brian Wong (IBG), Steph

Than
Please Note:

 Different uses will require different s

features defined in this addendum. Tformat, NOT a definition of different transactions.

 Automated algorithms can use the ex

features defined for a latent search wexplicitly computing them for the exeand thus it must be emphasized that extraction of the extended features oexemplar is not necessarily the only way to use this information. 
Next steps

1. Proposed Draft Addendum

1. Review by CDEFFS (current)2. Publicly available

2. Evaluation data

1. First set: BDM (NIST SD27)2. Second set: from FBI lab

3. Evaluation4. ANSI Canvass
Not all features are appro

all cases Both latent examiners and AFIS vendors have 

with additional features: 

 encoding time for the examiners R&D costs and processing complexity for the AFIS 

 Which features may be associated with relativ

improvements in accuracy cannot be known w

 We should assume that different implementat

would require some of the features, allow othand (possibly) ignore others.

 I suspect that the full set of CDEFFS features 

by hand in both the search and exemplar fingsituations such as challenged identifications opresentations.

 I suspect that AFIS searches will only use som

we have defined – which features should be urequires testing.
Sample Data

 “Ground truth” widely distributed datasets for 

evaluation

 For CDEFFS fingerprint data to exist, proof-of

client software needs to allow CDEFFS feature 

 we are just looking for manual, not automated CDE

to walk before we run)

 may require/request input from CDEFFS members/

features (e.g. ridge flow, skeletonized images, loca

 First set: the venerable BDM/SD27 latent set, 

CDEFFS

 Second set(s): 1000ppi sets, preferably with 

exemplar

 Such markup requires examiners: “Ground-tr

should have at least two examiners. Note thaexemplars need to be marked up.
Evaluations of effective

 The development of the test d

be a good sanity check to weefeatures (if any) that are not wpursuing, or need further defin

 Further testing seems to be a 

and-egg problem: effective tesrequires development of algorijustifying development of algopresumably requires some leveexpected benefit. 
Supporting Mate
List of Features (1 of 5)

ANSI/NIST File Format Fields

18.001 Logical Record Length18.002 Image Designation Character

Impression Type

18.003 Impression Type

Orientation Fields

18.004 Region of Interest18.005 Orientation
List of Features (2 of 5)

 Overall Image Characteristics 18.006 Physical Location(s) 18.007 Pattern Classification 18.008 Ridge Quality Map 18.009 Ridge Quality Map Format 18.01 Ridge Flow Map 18.011 Ridge Flow Map Format 18.012 Ridge Wavelength Map 18.013 Ridge Wavelength Map Format 18.014 Inverted Image 18.015 Degree of Distortion
List of Features (3 of 5)

 Reference Points  18.02 Cores 18.021 Deltas 18.022 Core-Delta Ridge Counts 18.023 Center Point of Reference 18.024 Distinctive Features Minutiae 18.03 Number of Minutiae 18.031 Minutiae 18.032 Minutiae Ridge Count Algorithm 18.033 Minutiae Ridge Counts
List of Features (4 of 5)

 Minor Features 18.04 Dots 18.041 Incipient Ridges 18.042 Creases and Linear Discontinuities 18.043 Ridge Edge Features 18.044 Area of Pore Characterization 18.045 Pores Annotations 18.05 Method of Feature Detection 18.051 Comments
List of Features (5 of 5)

 Skeletonized Image 18.996 Skeletonized Image Scale 18.997 Skeletonized Image Scale Units 18.998 Skeletonized Image Compression  18.999 Skeletonized Image
SWGFAST Concern

 “AFIS technology, since its onset, ha

very limited amount of fingerprint deprint experts must rely on far more ieffecting individualizations/exclusions ending ridges and bifurcations, i.e., tminutiae record. SWGFAST is attemeducate and provide to the vendor coadditional features and how they are these experts.”
1a: Ridge flow

 Adjacent frictio

directional arra

 Used to a limite

pattern classific

 Role of patter

diminishing as rolls to flats

 Some AFIS us

screening

 Already used b

verification maBioScrypt); usefor screening
1b: Cores and Deltas

 Cores and d

underutilizetechnology

 Core and de

shape, and all of use

 Using minu

high curvatsome of the 
1c: Finer level of classif

 The old Henry Classification was extremely 

latent print searching

 AFIS processing uses a simplified model, d

 the limited benefit a finer level of classific The difficulty of accurate automatic patte

this level
2a: Ridge Path (1 of 2)
2a: Ridge Path (2 of 2)

 A single ridge can 

if all these factors accounted for:

 Continuity Minutiae relationsh Curvature Relationships of no

features
2b: Open Field of Ridge



x
2b: Open Field of Ridge



x

 A matc

if the amarkedefinitwithouminuti

 The

min

 The

ther
2c: Greater definition of m

(1 of 3)

 Shape, size, and 

of minutiae are 

 Could use a fine

description of thshape and confiactual bifurcatio

 Use minutiae in 

endings and bifu

 Crossovers Trifurcations (etc)
2c: Greater definition of m

(2 of 3)

 Ridge endings 

in terms of 

 the fork of the 

valley, 

 the end of the 

ridge, and/or 

 the end of the 

end of the bin

 Bifurcations ca

as the same deblack-white rev

 Theta can be d

terms of differefrom the minut
2c: Greater definition of m

(3 of 3)

 Min

ridgflag

 Muc

intethan couneig

 Bina

tracprovbas
2d: Scars

 Presence, 

and configcan be verIF present 

 Linear ridg

discontinureadily be detected
2e: Creases

 Creases 

 between maj

skin (Flexure permanent aconfigurationbetween cap

 within friction 

can be permpermanent. 

 Flexure creases 

which provides direction to eaccrease. 
2f: Incipient ridges

 Friction ridges no

developed which shorter, thinner ior more intermittdeveloped friction 

 Rarely bifurcates Rarely/never has  May appear at ti

dots

 Shallower than o

 Often distinctive 

presence, and lochuman examiners
2g: Dots

 Dots, short ridge

enclosures are nused in IAFIS

 These are particu
3a: Pores

 Pores are distin

ways:

 Size Shape/form Position on th Number or fre

 Pores are not g

unless both finresolution of 1
3b: Edge shapes

 Morphological 

major deviatiothe contour or ridge edge 

 Major deviatio

discontinuities 500 ppi (thoubetter at 1000

 Edge features 

using Chatterjfeature classif
3c: Ridge/Valley width

 Measurements f

 Edge-to-edge o Edge-to-edge o Center-to-cente

ridges

 Special case of 

(3b)

 Possible to defin

 Actual width of 

valleys at regul

 Major deviation

Di ti iti