Automated Fingerprint Identification Systems

Goals of Seminar

Understanding of the basic concept of the AFIS

Database

Image Acquisition/mugshot/signature

Coder

Matcher

Arrest/Applicant Fingerprint and Palm Card Processing in AFIS

Two Finger vs. Tenprint File

Tenprint vs. Tenprint File

Tenprint vs. Unsolved Latent File

Expert Matching currently used in the state system

System Accuracy

Impact of agency workflow design

Current Issue’s/Limitations of AFIS

Mobile AFIS

Goals of Seminar

Brief description of a latent crime scene image.

Latent Acquisition in AFIS.

Latent Finger/Palm Searches

Latent vs. Latent Cognizant File/Latent vs. Unsolved Latent

File/Palm print vs. Unsolved Latent Palm

Latent Palm vs. Palm print Filet/

Latent Palm vs. Unsolved Latent PalmTP

Accuracy and how it is established in a latent system.

Image Resolution.

FBI IAFIS.

Differences in the state and FBI latent systems.

Current Issue’s/Limitations of latent search systems.

Automation of Fingerprint Search FBI / National Bureau of Standards Begin a Long Term Collaboration to Automate Fingerprint Search Process R&D Intentionally in the Public Domain FBI Introduces Initial Automated Processing Commercial AFIS Systems Emerge and are Rapidly Adopted Unable to Exchange Fingerprints Between Vendors FBI Processing Times and Backlogs Fall Behind States ANSI/NBS-ICST 1-1986 Fingerprint Identification - Data Format for Information Interchange FBI and Advisory Board Conceive National Search Hierarchy And Plan to Revitalize FBI Identification Services Advisory Policy Board Approves IAFIS System Requirements 1963 1979 1980’s 1986 1989 1992

AFIS Goals

Automation of the Fingerprint Search Process

Ability to effectively Search Crime Scene Images

Cardless System

____________________________

Benefits

Cost Savings (no files or storage required )

Ability to search Crime scene prints

Improve quality of prints captured

FBI Files 1997

AMERICAN INSTITUTE OF APPLIED SCIENCE

“ The basis of fingerprint identification is the premise that the configurations formed by the raised ridges of the palmar surface of the hands are unique and do not undergo any natural changes, except growth, from fetal life until decomposition.”

Ridge Characteristics a.k.a. Minutiae or Points

Specific traits found in friction ridges used to establish an identification by their relative location to each other.

The Average finger has between 75 and 175 points of identification. Palmar area approximately to 2,000 points of identification.

e.g. ending ridges, dots, bifurcations, islands

Core Systems in all Automated Fingerprint Identification Systems Fingerprint Input Coder Manual/system Matchers Database Finger/Palm Prints No matter what size the system, a small SPEX system or the FBI, they all have these Core components.

Latent System Latent fingerprint processing Latent palm print processing Unsolved latent fingerprint search Unsolved latent palm print search

Latent Input from Lincoln PD, Omaha PD, Douglas Co. and Stat Patrol

Input of Latent Print

The examiner will enter the latent examiner will enter the latent lift/photograph into the system. The image is designed for the fingerprint to be captured with the camera. The camera produces the better images. The scanner captures a larger area and is better suited for a latent palm. The scanner samples the image at intervals to create the image so using the scanner you do have a slight loss in image quality.

The Search (Coders & Matchers)

The examiner has a choice to manually select the points of identification (minutiae) or allow the system to select it.

Search database is approximately 350,000 records in Ne. and 60 million at the FBI.

Other helpful parameters that can be set are age, classification and gender.

Search takes 4-13 minutes depending on search parameters in Ne.

All 20 images rolled & flats are searched, if available.

Current system is 85% accurate when the latent has 15 points of identification and the target (exemplar) ten print card has the corresponding 15 points.

Avg. Latent has between 10 & 30 points.

Approximately 30% of crime scenes produce useable latent images. 21% of the crime scenes have latent fingerprints. The Other 9% are latent palm images

Search Results

The system uses expert matching to develop a list of viable candidates for the examiner to compare.

Accuracy ranges from 85% with the target print in the 1 st . 5 candidates to 88% that the target is in the top 15 candidates.

System creates a candidate list of the most likely suspects and the latent examiner determines hit or no hit.

If the lift is not identified it can be added to the unsolved latent file.

Lincoln PD, cold hit figures usually very close to Omaha figures

Latent Palm Print Searching

The new systems now have the ability to search latent palm images.

30% of the useable latent prints developed at a crime scene are palms.

Palm system accuracy is 70 to 90 % if the latent has 15 points of identification and the target (exemplar) ten print card has the corresponding 15 points.

Palms have in approximately 2,000 points of identification.

Palms have dedicated matchers and coders.

Application software supports 360 degrees search.

Lower palm (thenar, hypo-thenar and interdigital) Upper palm and Writers Latent palm searching will use a distribution point, segment, and hand areas to improve performance palm.

Unsolved Latent Files (latent finger & latent palm)

The latent finger and palm print files in Ne. are sized to hold 75,000 latent records including both latent fingerprints and latent palm prints.

Regardless of whether the record was entered from a ten print inked card or submitted via LSS, workflow capabilities are provided for automated search of the unsolved latent file.

When a new fingerprint card is processed through the ten print system and it is not identified the minutiae are compared with the unidentified latent stored in the UL file on AFIS.

Latent finger against the Unsolved accuracy 88-93%, a palm is 73-93%.

Accuracy Clarification

Accuracy figures used in this presentation are from a controlled environment and

are probably significantly higher than the results obtained in every day searching.

Th e r e are a number of factors that will influence accuracy:

Quality of the ten print card.

Quality of database , current database contains images captured at 500 ppi and 1,000 ppi.

Work flow

Quality of the examiner

Universal Latent Workstation

If a latent is serious enough it can be searched against the FBI IAFIS system. FBI system consists of :

The image captured for the state afis search can be used for the FBI search but new minutiae needs to be plotted.

50+ million arrest fingerprint cards from every state in the union at 500ppi.

Database penetration less than 30 %

Search database is created from the 1 st . Card received. The minutiae however is updated when subsequent cards are processed, so they have a composite of the best minutiae.

FBI has an unsolved latent file.

Core IAFIS Service Latent Submission

State Automated Fingerprint Identification Ten print Operation

New system purchased 2007 has approximately 20 Lives can Stations.

Lights Out

Quick ID & Verification

Mobile AFIS

Mugshot, Scars, Marks & Tattoos capability

Search Database Make Up

Copy of all/specific # of cards processed

Copy of the 1 st . Card processed (composite of all minutia plotted)

Composite of the best fingerprint images.

System Accuracy

System accuracy is influenced by:

Quality of images in the database

Workflow design

Training program

Proficiency testing/ remediation training

The FBI accuracy is 95 to 98%. The database

has the best quality images of any database in

the country. I would estimate the other state

and local databases around 90%

Lives can Unit

Today over 70% of the fingerprint cards are captured and digitized by lives can units.

Inked cards are the accepted at state level.

Images are captured at either 500 or 1,000 ppi

Quality Control Good quality finger/palm images are critical to the AFIS database. Prints sent from the coder are viewed by a trained technician. Thresholds monitored by the system administrator who deter- mines the amount of work going to Q.C The current system is designed for 5-10% of the transactions to go to Q.C

Advanced Fingerprint Processor (Coder)

1)The original captured fingerprint

2) The enhanced image is then “thinned” so that a “skeleton” image

3) Individual minutiae are then extracted from the thinned image

4) minutiae processing algorithm contains special procedures that minimize the number of “false” minutiae arising from over-inking, under inking, or smudges.

Matcher

The newer systems are now using the 8 best rolled images for the ten print search. Older systems oft used 2 fingers, NYC used the thumbs while NYS used the index fingers. The index and thumbs usually have large numbers of minutia so this was a pretty accurate system when storage costs were more significant.

Lights Out Functionality

This allows the system to make a determination of Identification or Non-Identification. Currently NYS is using this on non-criminal transactions with excellent results. It could be a way for a small state system like Nebraska to supplement ten print examiners. Us the lights out for the initial verification. The system administrator would be required to monitor thresholds more closely but it would improve system accuracy without increasing staffing requirements.

Ten print Results

The results of the ten print search are the basis

Used to:

Create criminal history (stat e rap, NCIC e tc.)

Insure no criminal history (applicant record check)

An error in either of the above can dramatically

impact public safety.

MUGSHOT/SMT CAPTURE All live scan sites in Nebraska are required to capture a mugshot at printing. This will allow th e stat e to move into Facial recognition in the future. In the current system mugshots are available to any latent workstation or live scan unit. This for example would allow Lincoln to get a mugshot from Scotts Bluff on a current arrest. Currently not all sits are following NIST guidelines for taking mugshot photographs. In the future all agencies will b required to follow the guidelines to share photo’s as well as creating a facial recognition database.

524984739075 DOE, JANE HENRYETTA

PHOTOS OF SMT’S

USE SAME CAPTURE TECHNIQUES AS MUG CAPTURE

CAPTURE VISIBLE SMT AND THOSE DISCLOSED DURING BOOKING PROCESS

MARKS REMOVED TATTOO SCARS TATTOO ARM , LEFT ARM , RIGHT FOOT , LEFT FOOT , RIGHT HAND , LEFT HAND , RIGHT HEAD , NON SPECIFIC HUMAN FORMS AND FEATURES ANIMALS AND ANIMAL FEATURES PLANTS FLAGS OBJECTS ABSTRACTIONS INSIGNIAS & SYMBOLS OTHER IMAGES BNATIONAL SYMBOLS POLITICAL SYMBOLS MILITARY SYMBOLS FRATERNAL SYMBOLS PROFESSIONAL SYMBOLS GANG SYMBOLS MISCELLANEOUS DOE, JANE HENRYETTA 524984739075

Quick ID/Verification