NIST ELFT-EFS 

Evaluation of Latent Fingerprint Technology — Extended Feature Sets

Evaluation 2 

Test Plan / API / Schedule 

12 April 2010 

Contents 

1 Overview ................................................................................................................................................. 1 

2 Participation ............................................................................................................................................ 2 

3 Data........................................................................................................................................................... 2 

3.1 Datasets.......................................................................................................................................... 2 

3.2 Format............................................................................................................................................ 3 

3.3 Features.......................................................................................................................................... 3 

3.4 Resolution...................................................................................................................................... 3 

3.5 Dimensions and orientation ....................................................................................................... 3 

3.6 Exemplar types ............................................................................................................................. 4 

3.7 Finger positions ............................................................................................................................ 4 

3.8 Dataset size.................................................................................................................................... 4 

4 Evaluation Criteria ................................................................................................................................. 4 

4.1 Performance Metrics .................................................................................................................... 4 

4.2 Evaluation Subtests...................................................................................................................... 5 

4.3 Reporting of Results..................................................................................................................... 5 

5 Latent Matching Software ..................................................................................................................... 5

5.1 Overview ....................................................................................................................................... 5 

5.2 Test Platform................................................................................................................................. 6 

5.3 Execution protocol ....................................................................................................................... 6 

5.3.1 Sequential ................................................................................................................................... 6 

5.3.2 Multithreaded ............................................................................................................................ 7 

5.4 API.................................................................................................................................................. 7 

5.4.1 Test Interface Description ........................................................................................................ 7 

5.4.2 Declarations ............................................................................................................................... 7 

5.4.3 NIST Provided Functions......................................................................................................... 9 

5.4.4 SDK Provided Functions........................................................................................................ 10 

5.4.5 Error Codes and Handling..................................................................................................... 14 

5.4.6 SDK Library and Platform Requirements ........................................................................... 15 

5.4.7 Installation and Usage ............................................................................................................ 15 

5.4.8 Documentation ........................................................................................................................ 16 

5.5 Software execution process....................................................................................................... 16 

5.6 Format of Candidate List .......................................................................................................... 16 

5.7 Validation .................................................................................................................................... 17 

5.8 Timing Requirements ................................................................................................................ 17 

6 Schedule and Software Submission Requirements ......................................................................... 17 

7 EFS Fields Used..................................................................................................................................... 18 

1 Overview 

The NIST Evaluation of Latent Fingerprint Technology — Extended Feature Sets (ELFT-EFS) is an

independently administered technology evaluation of latent fingerprint feature-based matching

systems. ELFT-EFS is being conducted by the National Institute of Standards & Technology 

(NIST). 

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ELFT-EFS is a complement to NIST’s Evaluation of Latent Fingerprint Technology (ELFT) testing

program. The ELFT evaluations to date have focused solely on automated feature extraction and

matching (AFEM) in the context of latent fingerprint identification. 

ELFT-EFS will evaluate the accuracy of latent matching using features marked by experienced

human latent fingerprint examiners. The purpose of this test is to evaluate the current state of the

art in latent feature-based matching, by comparing the accuracy of searches using images alone

with searches using different feature sets. The features sets will include the current IAFIS latent

feature set, and different subsets of the Extended Feature Set (EFS) features proposed by 

CDEFFS . A key result of the test is to determine when human feature markup is effective.

Because human markup is expensive in terms of time, effort, and expertise, there is a need to

know when image-only searching is adequate, and when the additional effort of marking 

minutiae and extended features is appropriate. 

1

The following summarizes the planned test: 

• The evaluation will involve 1:N searches using latent 1000ppi images provided with

human markup of EFS features. 

• Exemplars for the gallery will be images only. Exemplars will be 500ppi. 

• The test will be an SDK-type test, in that participants will provide software, and all

processing will take place on NIST hardware. 

• Different tests will be run for the following search types: 

o Image only 

o Image with region of interest markup 

o Image with minutiae (IAFIS EFTS LFFS equivalent) 

o Image with EFS features 

o Minutiae only (IAFIS EFTS LFFS equivalent) 

Test results will be made publicly available in a NIST report after the conclusion of the test. 

2 Participation 

Participation in Evaluation 2 is open to all developers of latent fingerprint identification systems. 

All systems must comply with the API outlined in Section 5.4. Anonymous participation will not

be permitted. The Application form2 includes details regarding application and qualification. 

3 Data 

3.1 Datasets 

Validation Dataset 

A Validation Dataset will be provided to participants before the evaluation to verify the correct

operation of participants’ software before and after delivery to NIST. 

Evaluation Dataset 

The latent and exemplar images and features in Evaluation #2 will be similar but not identical to

those in Evaluation #1. The Evaluation Dataset will contain sequestered data, formatted in the

same manner as the Validation Dataset. The Evaluation Dataset will contain Privacy Act or FOIA

Protected Information and will not be released to the participants or the public. The Evaluation

Dataset will to the extent permitted by law be protected under the Freedom of Information Act (5

U.S.C 552) and the Privacy Act (5 U.S.C. 552a) as applicable. 

1 CDEFFS is the ANSI/NIST Committee to Define an Extended Fingerprint Feature Set. The current

working draft of the Extended Fingerprint and Palmprint Features document can be found at

http://fingerprint.nist.gov/standard/cdeffs/. 2 The Application form can be found at http://fingerprint.nist.gov/latent/elft-efs/ 

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3.2 Format 

All images and data will be contained in ANSI/NIST files. All images will be 8-bit grayscale. 

Each latent ANSI/NIST file in the evaluation will contain one Type-1 record, one Type-2 record,

zero or one Type-9 records, and one Type-13 record. All latent images will be in Type-13 records,

in uncompressed format. 

Each exemplar ANSI/NIST file in the evaluation will contain one Type-1 record, and ten Type-14 

records (one for each finger, with finger positions identified). All exemplar images will be in

Type-14 records. 500ppi exemplar images will be compressed using WSQ. 

3.3 Features 

Files containing exemplars will not have any features defined: no Type-9 record will be present. 

Files containing latents may or may not have any features defined: zero or one Type-9 records

will be present. There will be tests comparing the accuracy of two primary types of searches: 

• Image-only searches, in which the latent image will not be accompanied by a type-9 

record. 

• Feature-based searches, in which the latent image will be accompanied by a type-9 record

with features defined in fields 9.300-9.372, formatted in accordance with “Data Format

for the Interchange of Extended Fingerprint and Palmprint Features,” abbreviated here

as the “EFS Spec” (Extended Feature Set Specification). The test will evaluate different

combinations of EFS fields, so not all EFS fields may be present in any given search. The 

subsets of features used (defined as Subsets LA-LG) are defined in Section 7. 

Note: The current EFS Spec version is 0.4 (June 2009). 

All of the latent IAFIS/EFS features will be provided with feature markup by human experts.

Note that all human markup will be conducted outside of ELFT-EFS and is not part of the

evaluation. 

Note also that conformance testing of automatic extraction of CDEFFS features is not part of this

test. In other words, the evaluation will not be measuring how close automatically extracted

features are to examiner created features. Automated algorithms can use the extended features

defined for a latent search without explicitly computing them for the exemplar image, and thus it

must be emphasized that automated extraction of the extended features on the exemplar is not

necessarily the only nor the best way to use this information. For example, an examiner may

mark an area as a scar; for the exemplar, the matcher would not necessarily have to mark the area

as a scar, but may use that information to match against a corresponding area with many false

minutiae and poor ridge flow. 

3.4 Resolution 

All latent images will be 1000 pixels per inch. 

Exemplar images will be at 500 pixels per inch. This resolution will be contained in field 14.009 

(Horizontal pixel scale), which will be identical to field 14.010 (Vertical pixel scale). 

3.5 Dimensions and orientation 

Latent fingerprint images may vary from 0.3”x 0.3” to 2.0” x 2.0” (width x height), all at 1000ppi.

1st & 3rd quartiles are about 700‐1200 pixels (width) or 900‐1400 pixels (height).

Exemplar images will be approximately upright (in the same orientation as they were captured). 

Neither latent nor exemplar images will be larger than 2.0” in either width or height. 

When orientation is known in advance or discernable by an examiner, latent fingerprint images

will not vary in orientation from upright more than ±45°. However, there are images in the

Evaluation Dataset for which orientation cannot be determined in advance, and all possible

orientations (±180°) must be considered equally likely. Images from latent subtests LB-LG will 

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4 Evaluation Criteria 

include the orientation direction and uncertainty fields (9.301). Images from latent subtest LA 

will not. 

3.6 Exemplar types 

All exemplars will include rolled or plain (segmented slap) fingerprints. The impression types

will include optical livescan and inked paper sources. The impression type will be noted in field

14.003. 

Exemplars will always include all ten fingers, and are therefore referred to here as a 10-finger

exemplar set (also commonly called a ten print set). 

Note that a 10-finger exemplar set will consist of either ten rolled prints, or ten plain prints. 

In some cases, multiple sets of 10-finger exemplar sets associated with one person will be

included in the gallery. This association will be made explicit in the exemplar enrollment stage: at

the time of enrollment, exemplars that are known to belong to the same person will always share

the same subject ID. 

3.7 Finger positions 

Exemplars will be provided in complete 10-finger sets, all contained within a single ANSI/NIST

file, with finger positions noted. 

The finger positions for latents will not be noted – no searches will be restricted to specific 

fingers. 

3.8 Dataset size 

The largest size gallery used for Evaluation 2 will contain 100,000 subjects having two 10-finger

exemplar sets (rolled and plain impressions) per subject. 

The total number of unique latent images is approximately 1,100, with the number of latent

searches based on section 4.2. 

4.1 Performance Metrics 

Performance metrics will be based on rank and matcher score: 

• Rank will be reported by the number of true matches reported in each position in the

candidate list. For example, the Rank-1 metric is the proportion of searches in which the

correct mate appears in the top position on the candidate list. CMC3 curves will also be 

reported to show how many latent images are correctly identified at rank 1, rank 2, etc. A

CMC is a plot of identification rate vs. recognition rank. Identification rate at rank k is the

proportion of the latent images correctly identified at rank K or lower. A latent image has

rank k if its mate is the kth largest comparison score on the candidate list. Recognition

rank ranges from 1 to 100, as 100 is the (maximum) candidate list size specified in the

API. 

• Matcher score metrics are evaluated in terms of DET/ROC4 performance, by plotting

False Positive Identification Rate (FPIR) and False Negative Identification Rate (FNIR) for

all score values. Note that this approach requires that a given matcher score be 

comparable between different latent searches. Both the absolute matcher score and the

probability of true match values (see Section 5.6) will be used for DET analysis. 

3 

4 

Cumulative Match Characteristic 

Detection Error Trade-off/Receiver Operating Characteristic 

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4.2 Evaluation Subtests 

The Evaluation is composed of the following subtests. For precise definitions of which features 

will be present for each subtest: see Section 7. All latents in each subtest may or may not be 

searched against all exemplars (galleries). Participants have the option of specifying which

specific subtest combinations they wish to be tested on. 

• Latent Subtests 

o LA – image only 

o LB – image + ROI 

o LC – image + ROI + Pattern Class + Quality Map 

o LD – image + IAFIS/EFTS equivalent features 

o LE – image + baseline EFS 

o LF – image + baseline EFS + Skeleton 

o LG – IAFIS/EFTS equivalent features only 

• Exemplar Subtests 

o E1 – 100,000 subjects; 1 set of 10 rolled and 1 set of 10 plain impressions each; 500ppi 

4.3 Reporting of Results 

The ELFT-EFS Final Report will contain descriptive information concerning the evaluation,

descriptions of each experiment, aggregate test results across all participants, and individual test

results for each participant. All results will be reported for each participating system, with the

exception of results for different combinations of EFS features. Because not all participating

systems may implement all of the EFS features, results from those evaluations will be stated in

generic terms so that participants cannot deduce which features are used by other systems. 

Note that the application form stipulates that each participant consents to the disclosure of its

performance. 

Enrollment, feature extraction and search timing information will also be reported, with the

explicit caveat that speed of execution, for both enrollment and latent search, is of secondary

importance. The report will specify the hardware specifications used in the evaluation, and will

also note that operational latent searching algorithms are likely to be implemented in more

sophisticated hardware. 

5 Latent Matching Software 

5.1 Overview 

Participants shall submit a set of SDKs (Software Development Kits) that provide the interfaces

defined by the ELFT-EFS API specified below. The SDKs shall be provided as static or dynamic

libraries to run on the NIST platform specified below. The ELFT-EFS API (Application

Programmer Interface) is modeled after the API from ELFT Phase 2. The most notable differences

from the ELFT Phase 2 API are that the exemplar and latent images and data provided to the

SDK will be contained in ANSI/NIST files, and exemplar feature extraction will process a single

exemplar per invocation (instead of the complete gallery). Also, the ELFT-EFS API specifies

operational time limits on a per-processor core basis, rather than per-machine. 

Each participant shall submit 

• one SDK for exemplar feature extraction and exemplar enrollment 

• one SDK for latent feature extraction 

• one SDK for latent 1-to-N search 

NIST recognizes the proprietary nature of the participant’s software and will take all reasonable

steps to protect this. The software submitted will be in an executable library format, and no

algorithmic details need be supplied. NIST agrees not to use the Participant’s software for

purposes other than indicated above, without express permission by the Participant. 

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5.2 Test Platform 

The NIST ELFT-EFS Evaluation test platform consists of an array of blade servers having a

hardware configuration similar to: 

Processor 

• Dual 2.8 GHz/1MB Cache, Xeon (dual-core) 

• 800 MHz Front Side Bus for PE 1855 

Memory 

• 16GB RAM (15GB available to applications) 

Secondary storage 

• 300GB 15K RPM Ultra SCSI Hard drives 

The operating systems available (in order of preference) are: 

• RedHat Enterprise Linux Server 5.1 (64-bit) 

• Windows 2008 Server (64-bit) 

• (Windows Server 32-bit may be available on request) 

The available RAM for 64-bit SDKs will be no more than 15GB total. The available RAM for 32-

bit SDKs will be no more than 3GB per process. 

5.3 Execution protocol 

Each SDK tested will be allocated multiple blades/cores from the array, along with a subset of

the test data in order to maximize (time) efficiency through parallel operation. 

Each SDK instance assigned to an individual blade or core will operate on a subset of the data,

using individual data copies (as needed) from a local storage device. 

For purposes of execution, there are two classes of SDKs, (1) sequential and (2) multithreaded.

And each class the SDK may utilize either 32 or 64-bit execution mode. Note that each SDK 

submitted (i.e. either of the two SDKs per participant) may be of a different class and execution mode. For 

example, the Exemplar feature extraction / enrollment SDK may be sequential 32-bit and the Latent feature

extraction / search SDK may be multithreaded 64-bit. 

It is highly recommended that SDKs implement multithreading using 64-bit execution mode. 

However, if some participants are unable to submit multithreaded or 64-bit SDKs, we support

other modes of operation as outlined below. 

5.3.1 Sequential 

An advantage of sequential (i.e. non-multithreaded) SDKs is the ability to “manually” parallelize

SDK execution for a given test by executing multiple instances per blade server (e.g. one per

core). A potential drawback is that individual 64-bit SDK instances have the potential to overallocate available RAM, which may result in “swapping,” decreasing overall execution speed.

Another potential drawback is contention for resources given that each instance is executing

independently (i.e. without coordinated resource usage). For this reason NIST does not 

recommend the submission of sequential SDKs. 

As a simple example, the execution of a sequential SDK for a subtest requiring M latent searches

against N exemplars (i.e. Gallery size N), may allocate M searches amongst K available cores such

that each core is executing M/K searches total. The primary choice here is whether or not to

allocate all cores available on a given blade server, or a subset thereof. How much memory is

allocated by the SDK (limited by whether it is 32 or 64-bit mode) is a primary consideration. 

Sequential SDKs which run in 32-bit execution mode shall have access to no more than 3GB per 

process. NIST will execute four (4) SDK instances (one instance per core) on each available blade

server, in order to maximize processor and memory utilization. 

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5.4.1 Test Interface Description 

 

 

Sequential SDKs which run in 64-bit execution mode shall have access of up to 15GB per process,

and the participant should inform NIST at submission time as to the SDK’s memory usage

requirements. It is strongly recommended that the SDK perform most efficiently when executed

as four (4) instances (one per core) on each blade server, where each instance allocates no more

than a quarter of available RAM (i.e. 3.75GB), as opposed to when executed as a single (1)

instance on each blade server which allocates all available RAM (i.e. 15GB). If more than 3.75GB 

is allocated per instance, the number of cores which can be utilized per blade server (without

swapping) is essentially 15GB divided by the amount of RAM allocated per SDK instance

(rounded to the nearest whole number). 

5.3.2 Multithreaded 

An advantage of multithreaded SDKs is the automatic utilization of available processor and

memory resources through parallelization (without need for “manual” scheduling). Another 

advantage is coordinated access (of each thread) to resources such as disk I/O. For this reason

NIST strongly recommends that submitted SDKs utilize multithreading aimed at maximizing

usage of 4 cores and run in 64-bit mode in order to have access of up to 15GB of RAM. 

As a simple example, the execution of a multithreaded SDK for a subtest requiring M latent

searches of N exemplars (i.e. Gallery size N), will allocate M searches amongst K available blades

such that each blade is executing M/K searches total. 

Multithreaded SDKs which run in 64-bit mode have full access to all cores and memory (15GB) 

on each allocated blade. This approach clearly makes use of processing resources, and has the

potential to mitigate contention issues through a coordinated use of parallelism. 

Multithreaded SDKs which run in 32-bit mode will be limited to 3GB of RAM per process, which

may limit their performance. Another option which exists here is for a multithreaded SDK to use

no more than 2 threads, where each SDK instance uses the maximum 3GB of RAM. If informed,

NIST could allocate two such SDKs per blade server in order to more fully utilize RAM. 

5.4 API 

Participants shall submit an SDK which provides the interfaces defined in section 5.4.4. Section 

5.4.3 defines the interfaces to functions provided by NIST for use by the SDK. Sections 5.4.2 and 

5.4.5 specify the declaration of constants, error codes, data-types and functions used by both. 

The software undergoing testing will be hosted on NIST-supplied computers. The executable

software under test will be built up from two sources: participant-supplied (SDKs) and NIST

supplied (image extraction library and test driver). 

5.4.2 Declarations 

The following are declarations of data types and functions used in the Latent Fingerprint SDK

testing interface: 

//////////////////////////////////////////////////////

// Declarations of constants //

////////////////////////////////////////////////////// 

// Impression type codes

#define IMPTYPE_LP 0 // Live-scan plain 

#define IMPTYPE_LR 1 // Live-scan rolled 

#define IMPTYPE_NP 2 // Nonlive-scan plain 

#define IMPTYPE_NR 3 // Nonlive-scan rolled 

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// Finger position codes

#define FINGPOS_UK 0 // Unknown finger

#define FINGPOS_RT 1 // Right thumb

#define FINGPOS_RI 2 // Right index finger

#define FINGPOS_RM 3 // Right middle finger

#define FINGPOS_RR 4 // Right ring finger

#define FINGPOS_RL 5 // Right little finger

#define FINGPOS_LT 6 // Left thumb

#define FINGPOS_LI 7 // Left index finger

#define FINGPOS_LM 8 // Left middle finger

#define FINGPOS_LR 9 // Left ring finger

#define FINGPOS_LL 10 // Left little finger 

////////////////////////////////////////////////////////////////

// Declarations for the NIST provided library functions //

//////////////////////////////////////////////////////////////// 

// Structure to hold a single fingerprint record (image+metadata)

struct finger_record

{ 

BYTE impression_type;

UINT16 resolution; // Image resolution in pixels/cm

BYTE finger_position;

UINT16 height; // Image height in pixels

UINT16 width; // Image width in pixels

BYTE *image_data; // 8-bit grayscale image data 

};

typedef struct finger_record FINGER_REC; 

// Extracts 10 fingerprint records from a ten-print (AN2K) file 

INT32 extract_image_data( const char *tenprint_filename,

FINGER_REC **finger_recs); 

// De-allocates the memory holding 10 fingerprint records 

void free_image_data(FINGER_REC *finger_recs); 

//////////////////////////////////////////////////////////////// 

// Declarations for the SDK provided library functions // 

//////////////////////////////////////////////////////////////// 

// Extracts features from exemplar 

INT32 extract_exemplar( const char *exemplarFilename, 

const char *outputDir); 

// Creates a gallery from set of extracted exemplar features 

INT32 create_gallery( const INT32 numExemplars, 

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const char **exemplarFeatFilenames, 

const char *galleryDir);

 // Selects the current gallery for latent searching 

INT32 set_gallery( const char *galleryDir); 

// Extracts features from latent file 

INT32 extract_latent( const char *latentFilename, 

 const char *outputDir); 

// Searches for the latent in the gallery 

INT32 latent_search( const char *latentFeatFilename, 

const char *outputDir); 

5.4.3 NIST Provided Functions 

5.4.3a Extract Image Data 

INT32 

extract_image_data(const char *tenprint_filename,

FINGER_REC **finger_recs); 

Description 

This function extracts ten fingerprint image records from a single (AN2K

formatted) ten-print record file. The caller shall pass tenprint_filename as a 

pointer to the fully qualified pathname of an AN2K formatted ten-print record

file, and finger_recs as the address of a pointer of type FINGER_REC (see 5.4.2 

above). 

Upon return finger_recs will contain a pointer to an array of ten FINGER_REC

structures ordered by finger position from 1 (right thumb) to 10 (left little finger).

For any fingers that are missing from the original ten-print record file, the

image_data field in the respective FINGER_REC will be a NULL pointer. 

Example

// Example of processing a ten-print record 

FINGER_REC *finger_recs; 

INT32 status=extract_image_data(“E000123.an2”, &finger_recs); 

if (status == 0) { 

for (i=0;i<10;i++) {

if (finger_recs[i].image_data != NULL)

process_valid_finger(finger_recs[i]); 

 else 

process_missing_finger(finger_recs[i]);

}

free_image_data(finger_recs); // see 5.4.3b below 

} 

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5.4.4a Exemplar Feature Extraction 

Parameters 

tenprint_filename (input): A pointer to a ten-print record filename. 

finger_recs (output): The address of a FINGER_REC pointer. 

Return Value 

This function returns zero on success or a documented non-zero error code 

otherwise. 

5.4.3b Free Image Data 

void 

free_image_data(FINGER_REC *finger_recs); 

Description 

De-allocates all memory used by the array of FINGER_REC structures specified 

by finger_recs which was allocated during a call to extract_image_data(). 

Parameters 

finger_recs (input): A pointer to an array of FINGER_REC structures. 

Return Value 

None. 

5.4.4 SDK Provided Functions 

INT32 

extract_exemplar( const char *exemplarFilename, 

const char *outputDir); 

Description 

This function produces a single proprietary formatted feature set file from a 10-

print exemplar set. The output from multiple calls to this function (i.e. multiple

proprietary feature set files) will be used to construct a gallery (see section 5.4.4b)

that is searchable by latent_search(). 

The 10-print exemplar set will be contained in an ANSI/NIST file with pathname

specified by exemplarFilename (e.g. “/mnt1/input/E1/E199999_1.an2”), and

that file will contain either 10 rolled or 10 segmented slap fingerprint images. 

The directory to which the proprietary feature set file shall be written is specified

by the pathname pointed to by outputDir (e.g. “/mnt/output/feats/E1/”). 

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The format of all pathnames will be canonical Unix style pathnames using

forward slash directory separators. The maximum total pathname length is 255 

characters. 

A single proprietary feature set file shall be written to the directory specified by

outputDir. No files other than the feature set file may be written. The filename of 

the output feature set file is defined here as the base filename of

exemplarFilename with the extension “.an2” replaced by “.feat” (no quotes). For 

example, if exemplarFilename =”/mnt1/input/E1/E199999_1.an2” and 

outputDir = ”/mnt/output/feats/E1/”, the proprietary feature set file shall be

written as “/mnt/output/feats/E1/E199999_1.feat”. 

No format is prescribed for the output feature data. For example if desired it

may contain images from the 10-print exemplar set. A feature file shall always be

output, regardless of any internal failures such as a failure of automated feature

extraction. The contents of the directory pointed to by outputDir (structure and 

other contents) are not relevant. Pre-computation of feature data avoids 

reprocessing of the original images upon subsequent calls to 

latent_search(). 

The SDK shall use the function extract_image_data() (see 5.4.3a above)

provided by NIST to extract the raw grayscale image and metadata from the 10-

print exemplar set file specified by exemplarFilename. Note that each call to 

extract_image_data() allocates memory to hold the extracted image and 

metadata, so this memory should be de-allocated using the NIST provided 

free_image_data() (see 5.4.3b above) function when no longer needed. 

Return Value 

This function returns zero on success or a documented non-zero error code 

otherwise. 

5.4.4b Gallery Creation 

INT32 

create_gallery( const INT32 numExemplars, 

const char **exemplarFeatFilenames, 

const char *galleryDir); 

Description 

This function writes a proprietary enrolled gallery to galleryDir (e.g. 

”/mnt/output/gallery/E1/”), based on a list of exemplar feature set file 

pathnames specified by exemplarFeatFilenames. The gallery shall be usable in 

read-only mode by subsequent calls to latent_search(), and shall associate 

all exemplar feature sets having the same subject ID (see below). The format of 

the gallery is at the discretion of the SDK provider. Subdirectories and multiple 

files may be created within galleryDir. All data produced by the SDK during the

execution of this function shall be stored exclusively to the directory specified by

galleryDir. 

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The list of exemplar feature set file pathnames is contained in 

exemplarFeatFilenames, which is an array of pointers having length 

numExemplars + 1, where each element of the array is a pointer to an exemplar

feature set file pathname. The last element of the array will be equal to 0 (i.e. a 

NULL pointer). 

The format of all pathnames will be canonical Unix style pathnames using

forward slash directory separators. The maximum total pathname length is 255

characters. 

Each exemplar feature set file pathname will be formatted dirPath“E”subjectID 

“_” instance “.feat” (no quotes or spaces), where dirPath is the full directory path 

of the file, subjectID is a 6-digit numeric ID (with leading zeros) uniquely 

identifying the subject, and instance is a 1-digit arbitrary numeric index to 

differentiate between multiple exemplar sets belonging to the same subject. For 

example, “/mnt/output/feats/E1/E199999_1.feat” 

Return Value 

This function returns zero on success or a documented non-zero error code 

otherwise. 

5.4.4c Set Gallery 

INT32 

set_gallery(const char *galleryDir); 

Description 

This function selects the gallery which shall be used by all subsequent calls to

latent_search(). The directory pathname specified by galleryDir (e.g.

”/mnt/output/gallery/E1/”) shall contain the gallery produced by a prior call

to create_gallery(). 

The format of the pathname will be canonical Unix style pathnames using

forward slash directory separators. The maximum total pathname length is 255

characters. 

Return Value 

This function returns zero on success or a documented non-zero error code 

otherwise. 

5.4.4d Latent Feature Extraction 

INT32 

extract_latent( const char *latentFilename, 

 const char *outputDir); 

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Description 

This function produces a single proprietary formatted feature set file from an

ANSI/NIST file containing a set of 0 or more manually extracted features and a

latent fingerprint image (except for subtest LG, see section 7). The proprietary

formatted feature set file output by this function will be used as input to

latent_search(). 

The ANSI/NIST file will be specified by a pathname pointed to by 

latentFilename (e.g. “/mnt1/input/L3/L12ABC.an2”). The directory to which

the proprietary feature set file shall be written is specified by the pathname

pointed to by outputDir (e.g. “/mnt/output/feats/L3/”). 

The format of all pathnames will be canonical Unix style pathnames using

forward slash directory separators. The maximum total pathname length is 255 

characters. 

A single proprietary formatted feature set file shall be written to the directory

specified by outputDir. No format is prescribed for the feature data. The feature 

data may include any or all manually extracted features already present in the

ANSI/NIST file (e.g. it may encode them in a proprietary format). For example if 

desired it may contain the latent fingerprint image. No files other than the 

feature set file may be written. A feature file shall always be output, regardless

of any internal failures such as a failure of automated feature extraction. The

filename of the output feature set file is defined here as the base filename of

latentFilename with the extension “.an2” replaced by “.feat” (no quotes). For 

example, if latentFilename = ”/mnt1/input/L3/L12ABC.an2” and outputDir = 

”/mnt/output/feats/L3/”, the proprietary feature set file shall be written as

“/mnt/output/feats/L3/L12ABC.feat”. 

Return Value 

This function returns zero on success or a documented non-zero error code on 

failure. 

5.4.4e Latent Search 

INT32 

latent_search( const BYTE *latentFeatFilename, 

const char *outputDir); 

Description 

This function searches the current gallery (as selected by set_gallery()) for 

zero or more candidates matching the input latent feature set (created by 

extract_latent()) whose pathname is specified by latentFeatFilename, and 

outputs a candidate list to the directory specified by outputDir. The format of 

the candidate list is specified in section 5.6. 

The selection of features on which to match is entirely at the discretion of the

SDK. Note that during the call to this function the directory containing the

current gallery and its contents are read-only. 

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The format of all pathnames will be canonical Unix style pathnames using

forward slash directory separators. The maximum total pathname length is 255

characters. 

One candidate list file (per call to this function) shall be written to the directory

specified by outputDir. A candidate list file shall always be output, regardless of

any internal software failures. The filename of the candidate list file is defined 

here as the base filename of latentFeatFilename with the extension “.feat” 

replaced by “.CL” (no quotes). For example, if latentFeatFilename = 

”/mnt1/output/feats/L3/L12ABC.feat” and outputDir = 

”/mnt/output/clists/L3/”, the candidate list file shall be written as 

“/mnt/output/clists/L3/L12ABC.CL”. 

Note 1: Since it may not be possible to keep all gallery data in memory, it might be

necessary for the software to repeatedly retrieve the data from disk, and this extra fetch

time will be included in the execution time measurement. 

Note 2: The candidate list shall only depend on the inputs to this function and the

currently selected gallery (not on any previous results from this function). Thus,

identical latent feature inputs and gallery data shall produce identical candidate lists

independent of all prior calls to this function. 

Return Value 

This function returns zero on success or a documented non-zero error code on 

failure. 

5.4.5 Error Codes and Handling 

The participant shall provide documentation of all (non-zero) error or warning return codes (see

section 5.4.8, Documentation). 

The application should include error/exception handling so that in the case of a fatal error, the

return code is still provided to the calling application. 

All messages which convey errors, warnings or other information shall be suppressed, except

where they may provide additional information not conveyable by the defined error codes alone

(such as listing a specific file related to the error). 

At minimum the following return codes shall be used. 

Return Function Explanation 

code 

0 All Success 

-1 extract_image_data() unable to open file 

-2 extract_image_data() Incorrect file format 

-3 extract_image_data() error parsing ten-print file 

-4 extract_image_data() error decompressing image 

-5 extract_image_data() insufficient memory error 

-6 extract_image_data() unspecified error 

100 extract_exemplar() exemplar file not found 

101 extract_exemplar() output directory not found 

102 extract_exemplar() unable to write feature data 

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103 extract_exemplar() error from extract_image_data (write to stdout) 

201 create_gallery() feature file not found (write filename to stdout) 

202 create_gallery() output directory not found 

203 create_gallery() unable to write gallery enrollment data 

204 create_gallery() insufficient memory available 

301 extract_latent () latent file not found 

302 extract_latent() output directory not found 

303 extract_latent() unable to write feature data 

401 set_gallery() gallery directory not found 

501 latent_search() gallery directory not set 

502 latent_search() insufficient memory available 

503 latent_search() feature file not found 

504 latent_search() candidate list directory not found 

505 latent_search() unable to write candidate list 

5.4.6 SDK Library and Platform Requirements 

Participants shall provide NIST with binary code only (i.e. no source code) − supporting files 

such as header (“.h”) files notwithstanding. 

Note that dependencies on external dynamic/shared libraries such as compiler-specific 

development environment libraries are discouraged. If absolutely necessary, external libraries

must be provided to NIST upon prior approval by the Test Liaison. 

The SDK will be tested in non-interactive “batch” mode (i.e. without terminal support). Thus, the

library code provided shall not use any interactive functions such as graphical user interface

(GUI) calls, or any other calls which require terminal interaction. 

The use of multi-threading by the SDK is encouraged as the NIST test platform includes dualprocessor dual-core support. The SDK need not be “thread safe” as the NIST test driver itself is 

single threaded. If multi-threading is utilized by the SDK is shall be documented. 

NIST will link the provided library file(s) to a C language test driver application (developed by

NIST) using the GCC compiler (for Windows platforms Cygwin/GCC version 3.3.1 will be used; for

Linux platforms GCC version 4.1.2 and GNU ld 2.17.50.0.6-5.el5 will be used. All GCC compilers use 

Libc 6). For example, 

gcc –o latenttest latenttest.c -L. –lelftEfsSDK 

Participants are required to provide their library in a format that is linkable using GCC with the

NIST test driver, which is compiled with GCC. All compilation and testing will be performed on

x86 platforms running either Windows 2008 Server or Red Hat Enterprise Linux Server release

5.1 “Tikanga” (kernel 2.6.18-53 or higher) dependent upon the operating system requirements of

the SDK. Thus, participants are strongly advised to verify library-level compatibility with GCC

(on an equivalent platform) prior to submitting their software to NIST to avoid linkage problems

later on (e.g. symbol name and calling convention mismatches, incorrect binary file formats, etc.). 

5.4.7 Installation and Usage 

The SDK must install easily (i.e. one installation step with no participant interaction required) to

be tested, and shall be executable on any number of machines without requiring additional

machine-specific license control procedures or activation. 

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The SDK’s usage shall be unlimited. No usage controls or limits based on licenses, execution

date/time, number of executions, etc. shall be enforced by the SDK. 

It is requested that the SDK be installable using simple file copy methods, and not require the use

of a separate installation program. Contact the Test Liaison for prior approval if an installation 

program is absolutely necessary. 

5.4.8 Documentation 

Complete documentation of the SDK shall be provided, and shall detail any additional 

functionality or behavior beyond what is specified in this document. The documentation must 

define all error and warning codes. 

The execution process will take place in three passes: 

• Exemplar feature extractions and Gallery creation 

• Latent image feature extractions 

• Latent searches against each Gallery 

5.6 Format of Candidate List 

The result of the latent_search() function is a candidate list, saved as a tab-delimited text file. The

candidate list has a fixed length of one hundred (100) candidates. The candidate list consists of

two parts, a required and an optional part. 

The required part consists of: 

• the ID of the mating exemplar subject 

• the matching finger number 

• the absolute matching score 

• an estimate of the probability of a match (0 to 100) 

The optional part consists of: 

• the number of minutiae identified in the latent 

• the number of latent minutiae which were successfully matched 

Sample Candidate List 

Required Part Optional Part 

Rank Mate ID Finger 

No. 

Abs. 

Score 

Prob. Of 

True 

Match 

No. Latent 

Minutiae 

Minutiae 

Matched 

1 073141 2 3513 93 18 12 

2 199999 2 605 5 18 5 

3 004334 3 513 4 18 5 

... 

100 920792 9 422 1 18 4 

Table 1: Sample candidate list 

The candidate list is ordered based upon the absolute score, with the highest score in the first

position. 

The parameter Probability of True Match is an estimate of the probability that the candidate is a

true match. Its values range from 0 to 100. 

Each candidate list will be stored in an individual tab-delimited ASCII text file. Within the 

candidate list file, all required and optional parts for an individual candidate entry (i.e. row)

should be written one per-line in the order shown above, with each part (i.e. column) separated

by a single tab character. 

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Note that “Mate ID” shall be written as the 6-digit subjectID (see section 5.4.4b) part of the 

exemplar filename specified to the create_gallery() function. E.g. if 

“/mnt/output/feats/E1/E199999_1.feat” was enrolled to the gallery being searched, the Mate ID

shall be “199999”, without quotes). Note also that the candidate list refers to a subject and finger

position, not a specific exemplar impression. 

5.7 Validation 

As discussed in Section 3.1, a Validation Dataset will be provided to verify the correct operation

of participants’ software before and after delivery to NIST. Using this data and the submitted

SDK, identical outputs must be generated by NIST to those submitted by participants in order for

the submitted SDK to be accepted. Acceptance of the submitted SDK must occur prior to the 

deadlines specified in section 6. 

The Validation Dataset will be a small subset of the ELFT-EFS Public challenge dataset. 

5.8 Timing Requirements 

The ELFT-EFS Evaluation test must place limits on the processing time of the major operations

involving feature extraction and enrollment (exemplars and latents) and searching. There are 

two purposes for such limits. The first is to enable practical execution of the test within an 

acceptable period of time. The second is to measure performance at throughput rates comparable

to large-scale operational scenarios. Our sponsors have interest in relevance of results to nearterm operational requirements. The size of the test will be dictated to a large extent by these

throughput numbers. 

SDK time limits are specified on a "per-core" basis, meaning that the specified operational rates

are for a single core – in other words, rates will be specified from the perspective of a sequential

process executing on a single CPU core. For example, if the specified rate for latent search is R

exemplars per second, then a multithreaded SDK instance operating on 4 cores must achieve an

aggregate rate of 4 x R. All time limits below are averages with respect to the hardware used on

the NIST test platform specified above. 

The search time requirements specified below are for Subtests LC-LG: see Section 7 for details. It 

is recognized that for some implementations, throughput for image�only searches (Subtest LA)

may be slower due to less effective screening. It is allowable for throughput on Subtest LA 

(image only) and LB (image+ROI) to be slower by a factor of up to 2x than the stated search time.

Proposed time limits for the ELFT-EFS Evaluation are (per single CPU core): 

Exemplar feature extraction 100 sec/10-finger exemplar set (rolled or pre-segmented slap) 

Latent enroll 120 sec/latent 

Search 0.025 sec/10-finger exemplar set 

Rate of 40 exemplar sets/sec, per latent (exemplar set = 10 all rolled or all plain prints) 

Table 2: Timing requirements 

6 Schedule and Software Submission Requirements 

To enable enrolling the gallery before the evaluation itself takes place, we are requesting the

exemplar feature extraction/enrollment SDKs prior to the latent feature extraction/search SDKs.

For each SDK, we have both early and final deadlines: we will accept SDKs as early as the early

deadline, and will use the period from receipt of the SDKs until the final deadline to validate

correct operation of the SDKs, but must have fully operational software by the final deadline.

Between the early and final deadlines, we will report any software issues encountered, and will

accept software replacements. 

If major software problems arise during the execution of the evaluation (i.e. after the submission

deadline), reasonable attempts will be made to resolve the issue(s) through reporting and receipt

of replacement software. However replacement software must not include algorithm 

enhancements beyond those addressing the specific problem(s) reported. 

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7 EFS Fields Used 

Registration/Withdraw

• Registration form online: 12 April 2010

• Registration deadline: 15 May 2010

• Deadline for anonymous withdraw: 21 June 2010

Exemplar feature extraction / enrollment SDKs:

• Submission Period Open: 15 May 2010

• Final deadline: 21 June 2010

Latent feature extraction / search SDKs:

• Submission Period Open: 15 May 2010

• Final deadline: 21 June 2010

Subtest combinations for ELFT-EFS Evaluation 1 

Abb. # Field Name 

Subtest 

LA: 

Image 

only 

Subtest 

LB: 

ROI 

Subtest 

LC: 

ROI, 

Pattern 

Class, 

Quality 

Map 

Subtest 

LD: 

IAFIS/ 

EFTS 

equivalent 

Subtest 

LE: 

Baseline 

EFS 

Subtest 

LF: 

Baseline 

EFS 

with 

Skeleton 

Subtest 

LG: 

IAFIS/ 

EFTS 

equivalent 

With Image Without 

Image 

LEN 9.001 Logical Record 

Length Yes Yes Yes Yes Yes Yes Yes 

IDC 9.002 Image Designation 

Character 

Yes Yes Yes Yes Yes Yes Yes 

IMP 9.003 Impression Type 

Yes Yes Yes Yes Yes Yes Yes 

FMT 9.004 Minutiae Format 

Yes Yes Yes Yes Yes Yes Yes 

ROI 9.300 Region of Interest Yes Yes Yes Yes Yes Yes 

ORT 9.301 Orientation Yes Yes Yes Yes Yes Yes 

FPP 9.302 Finger/Palm 

Position(s) 

PAT 9.307 Pattern Classification Yes Yes (**) Yes Yes Yes (**) 

RQM 9.308 Ridge Quality Map Yes Yes Yes 

RQF 9.309 Ridge Quality Map 

Format Yes Yes Yes 

RFM 9.310 Ridge Flow Map Yes 

RFF 9.311 Ridge Flow Map 

Format Yes 

RWM 9.312 Ridge Wavelength 

Map 

RWF 9.313 Ridge Wavelength 

Map Format 

TRV 9.314 Tonal Reversal Yes Yes Yes Yes Yes Yes 

PLR 9.315 Possible Lateral 

Reversal 

FQM 9.316 

Friction Ridge Quality 

Metric 

PGS 9.317 Possible Growth or 

Shrinkage 

COR 9.320 Cores Yes Yes Yes Yes 

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DEL 9.321 Deltas Yes Yes Yes Yes 

CDR 9.322 Core-Delta Ridge 

Counts Yes Yes Yes Yes 

CPR 9.323 Center Point of 

Reference Yes Yes 

DIS 9.324 Distinctive 

Characteristics Yes Yes 

NCR 9.325 No Cores Present Yes Yes 

NDL 9.326 No Deltas Present Yes Yes 

NDC 9.327 No Distinctive Areas 

Present Yes Yes 

MIN 9.331 Minutiae Yes (*) Yes Yes Yes (*) 

MRA 9.332 Minutiae Ridge Count 

Algorithm 

MRC 9.333 Minutiae Ridge 

Counts Yes Yes Yes Yes 

NMP 9.334 No Minutiae Present Yes Yes 

RCC 9.335 Ridge Count 

Confidence Yes Yes 

DOT 9.340 Dots Yes Yes 

INR 9.341 Incipient Ridges Yes Yes 

CLD 9.342 Creases and Linear 

Discontinuities Yes Yes 

REF 9.343 Ridge Edge Features Yes Yes 

NPP 9.344 No Pores Present Yes Yes 

POR 9.345 Pores Yes Yes 

NDT 9.346 No Dots Present Yes Yes 

NIR 9.347 No Incipient Ridges 

Present Yes Yes 

NCR 9.348 No Creases Present Yes Yes 

NRE 9.349 No Ridge Edges 

Present Yes Yes 

MFD 9.350 Method of Feature 

Detection 

COM 9.351 Comments 

LPM 9.352 Latent Processing 

Method 

EAA 9.353 Examiner Analysis 

Assessment Yes Yes 

EOF 9.354 Evidence of Fraud 

LSB 9.355 Latent Substrate 

LMT 9.356 Latent Matrix 

LQI 9.357 Local quality issues Yes Yes 

AOC 9.360 Area of 

Correspondence 

CPF 9.361 Corresponding Points 

or Features 

ECD 9.362 Examiner 

Comparison 

Determination 

SIM 9.372 Skeletonized Image Yes (***) 

RPS 9.373 Ridge Path Segments 

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