NBIS指南：指纹识别入门与NIST非出口受限软件安装详解

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指纹识别在信息技术领域具有广泛的应用，尤其是在生物特征识别技术中占据核心地位。本文档，"iUser's Guide to NIST Biometric Image Software (NBIS)"，由美国国家标准与技术研究院（NIST）的多位专家共同编撰，旨在为初学者提供关于非出口管制指纹图像软件NBIS的安装和执行指南。该软件的开发得到了联邦调查局（FBI）和国土安全部的资助，目标是支持指纹等生物特征数据的处理与分析。作为一篇用户手册和参考文献，文章详尽地解释了如何安装和使用NBIS，特别强调了软件在实际操作中的应用流程。虽然文中包含了一些算法概述，但更深入的细节则需要参考文献进行查阅。文档的目录设计使得读者能够快速定位所需的信息，并且电子版本中的超链接功能使得导航更为便捷。 NIST Biometric Image Software (NBIS)是一个关键工具，它可能包含指纹识别的基本原理，如 minutiae analysis（指纹纹路特征分析），模板匹配技术，以及可能的算法优化，如 minutiae-based verification（基于 minutiae 的验证）和 enrollment（注册）过程。此外，还可能涵盖了如何处理指纹采集、噪声去除、特征提取、匹配比对等方面的技术，这些都是现代生物识别系统的核心组成部分。对于初学者来说，这篇指南不仅提供了实践指导，还可能涉及一些基础概念的阐述，如指纹特征的标准化、质量评估，以及与硬件设备的集成等。学习者可以通过阅读和实践来理解指纹识别的完整流程，包括数据预处理、特征提取、以及在安全系统中的实际应用。 "Fingerprint Recognition IEEE" 文件提供了NIST Biometric Image Software（NBIS）的入门教程，对于希望了解和入门指纹识别技术的人来说，无论是理论知识学习还是实际操作技能提升，都是一份宝贵资源。通过深入研究，读者可以掌握这项技术在信息安全、司法鉴定以及其他依赖于生物特征认证场景中的关键作用。

Orientation arrays or matrices like the ones used in PCASYS were produced in early fingerprint

work at Rockwell, CALSPAN, and Printrak. The detection of local ridge slopes came about

naturally as a side effect of binarization algorithms that were used to preprocess scanned

fingerprint images in preparation for minutiae detection. Early experiments in automatic

fingerprint classification using these orientation matrices were done by Rockwell, improved

upon by Printrak, and work was done at NIST (formerly NBS). Wegstein, of NBS, produced the

R92 registration algorithm that is used by PCASYS and did important early automatic

classification experiments.[8]

The algorithms used in PCASYS are described further in Section 5.1 and in References [17] and

[22]-[24].

3.2. MINDTCT – Minutiae Detection

Another software system provided in this distribution is a minutiae detection package called,

MINDTCT. This section first describes what fingerprint minutiae are, and then some background

is provided as to why this package was developed for the FBI.

3.2.1. Definition of Minutiae

Traditionally, two fingerprints have been compared using discrete features called minutiae.

These features include points in a finger's friction skin where ridges end (called a ridge ending)

or split (called a ridge bifurcation). Typically, there are on the order of 100 minutiae on a

tenprint. In order to search and match fingerprints, the coordinate location and the orientation of

the ridge at each minutia point are recorded. Figure 3 shows an example of the two types of

minutiae. The minutiae are marked in the right image, and the tails on the markers point in the

direction of the minutia's orientation.

Figure 3. Minutiae: bifurcation (square marker) and ridge ending (circle marker).

3.2.2. Latent Fingerprints

In addition to tenprints, there is a smaller population of fingerprints also important to the FBI.

These are fingerprints captured at crime scenes that can be used as evidence in solving criminal

cases. Unlike tenprints, which have been captured in a relatively controlled environment for the

expressed purpose of identification, crime scene fingerprints are by nature incidentally left

behind. They are often invisible to the eye without some type of chemical processing or dusting.

It is for this reason that they have been traditionally called latent fingerprints.

As one would expect, the composition and quality of latent fingerprints are significantly different

from tenprints. Typically, only a portion of the finger is present in the latent, the surface on

which the latent was imprinted is unpredictable, and the clarity of friction skin details are often

blurred or occluded. All this leads to fingerprints of significantly lesser quality than typical

tenprints. While there are 100 minutiae on a tenprint, there may be only a dozen on a latent.

Figure 5 shows a "good" quality latent on the left and its matching tenprint on the right.

Figure 5. Latent fingerprint (left) with matching tenprint (right).

Due to the poor conditions of latent fingerprints, today's AFIS technology operates poorly when

presented a latent fingerprint image. It is extremely difficult for the automated system to

accurately classify latent fingerprints and reliably locate the minutiae in the image.

Consequently, human fingerprint experts, called latent examiners, must analyze and manually

mark up each latent fingerprint in preparation for matching. This is a tedious and labor intensive

task.

To support the processing of latent fingerprints, the FBI and NIST collaboratively developed a

specialized workstation called the Universal Latent Workstation (ULW). This workstation has

been designed to aid the latent examiner in preparing a latent fingerprint for search. In addition,

the workstation provides for interoperability between different AFIS systems by functioning as a

vendor-independent front-end interface. These two aspects of the ULW contribute significantly

to the advancement of the state-of-the-art in latent fingerprint identification and law enforcement

in general. As such, the FBI has chosen to distribute the ULW freely upon request. To receive

more information regarding ULW, please contact:

Tom Hopper (thopper@leo.com)

FBI, JEH Bldg.

CJIS Div / Rm 11192E

935 PA Ave., NW

Washington, DC 20537-9700

202-324-3506

The successful application of the ULW is primarily facilitated by its use of the ANSI/NIST-ITL

1-2000 standard. NIST also developed some its underlying core technology, including the

minutiae detection package in this software distribution.

MINDTCT takes a fingerprint image and locates all minutiae in the image, assigning to each

minutia point its location, orientation, type, and quality. The command, mindtct, reads a

fingerprint image from an ANSI/NIST, WSQ, baseline JPEG, lossless JPEG file, or IHead

formatted file. A command line option [-b] can be selected that allows mindtct to perform

image enhancement on low contrast images. Next it detects the minutiae in the image and

outputs them to a file.

If the input file format was ANSI/NIST, it encodes the results into a Type-9 minutiae record

[30], combines the record with the input data, and writes it all out to a new ANSI/NIST file. If

the input file is not ANSI/NIST, the results are printed to a formatted text file. Details of this are

included in the mindtct man page in APPENDIX C. The default is for mindtct to output the

minutiae based on the ANSI/NIST standard as described in Section 3.2.1 but it has the option to

write the minutiae according to the M1 (ANSI INCITS 378-2004) representation. M1 has the

pixel origin in the top left of the image and directions pointing up the ridge ending or bifurcation

valley consistent with the IAFIS minutiae representation illustrated in Figure 3.

An algorithmic description of MINDTCT is provided in Section 5.2.

3.3. NFIQ – Fingerprint Image Quality

Fingerprint image quality is a valuable tool in determining a fingerprint matchers ability to match

a fingerprint against other fingerprints. It has been shown that as fingerprint image quality

degrades so does matcher performance.[57][59] Knowing the fingerprint image quality can

dramatically change the way fingerprints are currently processed. For example, multiple

attempts could be made to obtain a higher quality fingerprint at the time of image capture. If the

quality of fingerprints were know in advance, a system could handle the fingerprints of different

quality in different ways. For example, a faster matcher could be used on good quality images

without a drop in accuracy and a slower more accurate matcher could be used on lower quality

images to improve matching accuracy. This would greatly improve the overall performance of

the system.

NFIQ is an implementation of the NIST “Fingerprint Image Quality” algorithm as described

in.[57] It computes a feature vector using the quality image “map” and minutiae quality statistics

produced by the MINDTCT minutiae detection algorithm. The feature vector is then used as

inputs to a Multi-Layer Perceptron (MLP) neural network classifier, and the output activation

level of the neural network is used to determine the fingerprint’s image quality value. There are

five quality levels with 1 being the highest quality and 5 being the lowest quality.

Please note that NFIQ has been designed to assign a level of quality to a grayscale fingerprint

image. It was not designed to distinguish fingerprint images from all possible non-fingerprint

images, and it will not necessarily assign non-fingerprint image types to the worst quality level

five.

A detailed description of the NFIQ algorithm is included in Section 5.3.

3.4. AN2K7 – Standard Reference Implementation

The AN2K package is a reference implementation of the ANSI/NIST-ITL 1-2007 standard.[30]

This package contains utilities for reading, writing, and manipulating the contents of ANSI/NIST

data files. These files are comprised of a sequence of data fields and image records. Source

code is provided to parse ANSI/NIST files into memory, manipulate designated fields, and write

the sequence back to file. The utility an2k7tool does this in batch mode. Logical data units

are referenced on the command line, and the specified contents may be printed, inserted,

substituted, or deleted from the file.

Alternatively, two other utilities are provided to support interactive editing of the contents of an

ANSI/NIST file. The command an2k7totxt converts the textual content of an ANSI/NIST file

into a formatted text report, and any binary data (including images) are stored to temporary files

and externally referenced in the text report. In this way, the text report can then be loaded into

any common text editor and ASCII information can be added, deleted, or changed. When all

edits are complete, the command txttoan2k7 is run on the edited version of the text report,

externally referenced binary data files are incorporated, and a new ANSI/NIST file is written.

One of the many types of records in an ANSI/NIST file is the Type-9 record designed to hold

minutiae data for fingerprint matching. Currently there is no global consensus on how

fingerprint minutiae should be numerically represented. Different fingerprint systems use

different sets of attributes and representation schemes. To manage this, the fields of the Type-9

record have been divided into blocks, where each block is assigned to a registered vendor, and

the vendor defines how he will represent his minutiae data. In the standard, the first 4 fields of

the Type-9 record are mandatory and must always be filled. Fields 5 through 12 are fields in the

standard defined by NIST to hold among other things, the fingerprint’s core, delta, and minutiae

locations, along with neighbors and intervening ridge counts. The FBI's IAFIS is assigned fields

13 through 23. The definition of these fields is specified in the FBI's Electronic Fingerprint

Transmission Specification (EFTS).[37]

Unfortunately, these two blocks of fields are different. Two utilities are provided in the AN2K7

package to facilitate the conversion between these blocks of fields in a Type-9 record. The

command an2k7toiaf translates the minutiae data stored in NIST fields 5-12 into the

FBI/IAFIS fields 13-23. The command iaftoan2k7 reverses the process. An X Windows

ANSI/NIST file image previewer is included in the package. The utility dpyan2k7 is designed

to parse an ANSI/NIST file, locating and displaying each image in the file to a separate window.

In addition, if any minutiae are included in a corresponding Type-9 record, then the minutiae

points are plotted on top of the fingerprint image.

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