This publication is available free of charge from http://dx.doi.org/10.6028/NIST.IR.8017

NISTIR 8017

A Measurement Metric for Forensic 

Latent Fingerprint Preprocessing 

Haiying Guan

Andrew Dienstfrey

Mary Theofanos

Brian Stanton

http://dx.doi.org/10.6028/NIST.IR.8017
This publication is available free of charge from http://dx.doi.org/10.6028/NIST.IR.8017

NISTIR 8017

A Measurement Metric for Forensic 

Latent Fingerprint Preprocessing

Haiying Guan

Brian Stanton

Information Access Division

Information Technology Laboratory

Andrew Dienstfrey

Applied and Computational Mathematics Division

Information Technology Laboratory

Mary Theofanos

Office of Data and Informatics

Material Measurement Laboratory

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.IR.8017

July 2014

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie E. May, Acting Under Secretary of Commerce for Standards and Technology and Acting Director
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TABLE OF CONTENTS

1 INTRODUCTION......................................................................................................................................1

1.1 FORENSIC LATENT FINGERPRINT..............................................................................................................1

1.2 LATENT FINGERPRINT PREPROCESSING.....................................................................................................1

1.3 THE SOFTWARE AND TOOLS FOR LATENT FINGERPRINT PREPROCESSING...................................................4

2 METHOD....................................................................................................................................................5

2.1 LITERATURE REVIEW................................................................................................................................5

2.2 METHOD OVERVIEW.................................................................................................................................5

2.3 SIVV ON FLAT/TEN-PRINT OR ROLLED FINGERPRINT................................................................................6

2.4 MOTIVATION OF OUR APPROACH............................................................................................................10

2.5 BLACKMAN WINDOW..............................................................................................................................12

2.6 REGION OF INTEREST (ROI).....................................................................................................................15

2.7 PEAK LOCATION CONSTRAINT ................................................................................................................17

2.8 SOME PROPERTIES OF THE SIVV ............................................................................................................19

2.8.1 Image inversion............................................................................................................................19

2.8.2 SIVV on noise images..................................................................................................................22

3 EXPERIMENTAL RESULTS ................................................................................................................24

3.1 LATENT FINGERPRINT PREPROCESSING DATASET ...................................................................................24

3.2 LATENT FINGERPRINT QUALITY MEASUREMENT.....................................................................................26

3.2.1 Region of interest .........................................................................................................................26

3.2.2 Peak location constraint ...............................................................................................................27

3.3 COMPARISON RESULTS ...........................................................................................................................30

4 DISCUSSION AND FUTURE WORK...................................................................................................31

5 ACKNOWLEDGEMENTS.....................................................................................................................33

6 DISCLAIMER..........................................................................................................................................33

7 REFERENCES .........................................................................................................................................33

APPENDIX A: THE SIVV PEAK ON SYNTHETIC IMAGES ...................................................................37
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LIST OF TABLES AND FIGURES

TABLE 1: THE COMPARISON OF DIFFERENT ALGORITHMS WITH DIFFERENT OPTIONS ............................................31

TABLE 2: SIVV PEAK LOCATIONS ON THE IMAGES WITH THE STRIPES PATTERN (IMAGE SIZE 512×512 PIXELS) ...40

TABLE 3: SIVV PEAK LOCATIONS ON THE LARGE IMAGES WITH THE STRIPES PATTERN (IMAGE SIZE 1024×1024

PIXELS)........................................................................................................................................................41

TABLE 4: THE RELATIONSHIP BETWEEN PEAK LOCATIONS AND PPI......................................................................41

TABLE 5: THE PEAK LOCATIONS GIVEN THE STRIPE PIXEL DISTANCE. ...................................................................41

FIGURE 1: AN EXAMPLE OF FORENSIC LATENT FINGERPRINT PREPROCESSING. .......................................................2

FIGURE 2: THE NECESSITY OF LATENT FINGERPRINT PREPROCESSING....................................................................3

FIGURE 3: DISTINCT ENDPOINTS OF FORENSIC LATENT FINGERPRINT PREPROCESSING............................................4

FIGURE 4: SIVV ALGORITHM..................................................................................................................................8

FIGURE 5: AN EXAMPLE OF SIVV FEATURE ON A ROLLED FINGERPRINT IMAGE. ....................................................9

FIGURE 6: AN EXAMPLE OF THE SIVV FEATURE ON A LATENT FINGERPRINT IMAGE.............................................10

FIGURE 7: EXAMPLES OF THE SIVV FEATURE ON LATENT FINGERPRINT IMAGES..................................................12

FIGURE 8: BLACKMAN WINDOW (Α = 0.16)..........................................................................................................13

FIGURE 9: THE COMPARISON OF SIVV CURVES WITH DIFFERENT INPUT OPTIONS.................................................14

FIGURE 10: THE SELECTION OF REGION OF INTEREST...........................................................................................15

FIGURE 11: AN EXAMPLE OF REGION OF INTEREST...............................................................................................16

FIGURE 12: AN EXAMPLE OF SIVV PEAK LOCATION CONSTRAINT........................................................................18

FIGURE 13: THE ORIGINAL IMAGE AND ITS INVERT IMAGE OF WHITE POWER DEVELOPED PRINTS.........................20

FIGURE 14: THE COMPARISON OF SIVV CURVES OF AN IMAGE AND ITS INVERT IMAGE........................................21

FIGURE 15: THE IMAGE WITH DIFFERENT LEVELS OF IID RANDOM NOISE. ............................................................22

FIGURE 16: THE IMAGE MATRICES WITH RANDOM NOISE......................................................................................23

FIGURE 17: THE COMPARISON OF SIVV CURVES OF AN IMAGE WITH DIFFERENT LEVELS OF IID RANDOM NOISE. 24

FIGURE 18: THE LATENT FINGERPRINT PREPROCESSING IMAGE PAIRS FROM A TRAINING COURSE (SIX TYPES). ...25
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NISTIR 8017 Page iv 7/2014

FIGURE 19: THE PROPOSED ALGORITHM FOR LATENT FINGERPRINT QUALITY MEASUREMENT.............................26

FIGURE 20: THE COMPARISON OF WHOLE IMAGE VS. ROI IMAGE. ........................................................................27

FIGURE 21: PEAK LOCATION CONSTRAINT. ...........................................................................................................28

FIGURE 22: PROPOSED PROCEDURE FOR LATENT FINGERPRINT SIVV FEATURE DETECTION. ................................29

FIGURE 23: THE FREQUENCY FEATURE OF LATENT FINGERPRINT USING THE PROPOSED ALGORITHM. ..................30

FIGURE 24: QUANTITATIVE COMPARISON OF LATENT FINGERPRINT IMAGE QUALITY. ..........................................32

FIGURE 25: SIVV ON THE IMAGES WITH THE STRIPES PATTERN (IMAGE SIZE 512×512 PIXELS)............................39

FIGURE 26: SIVV ON THE LARGE IMAGES WITH THE STRIPES PATTERN (IMAGE SIZE 1024×1024 PIXELS).............40

FIGURE 27: SIVV ON THE IMAGES WITH SQUARE PATTERN. .................................................................................42
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EXECUTIVE SUMMARY

Although fingerprint mark-up and identification are well-studied fields, forensic fingerprint 

image preprocessing is still a relatively new domain in need of further scientific study and 

development of best practice guidance. Latent fingerprint image preprocessing is a common 

step in the forensic analysis workflow that is performed to improve image quality for 

subsequent identification analysis while simultaneously ensuring data integrity. Due to the 

low quality of the latent fingerprint images, preprocessing is especially crucial to the success 

of the final fingerprint identification in the forensic fingerprint image examination. In this 

report, we isolate the forensic fingerprint image preprocessing step for more detailed 

analysis.

First we provide a brief review of latent fingerprint image preprocessing. We then turn to the 

problem of defining an image-based quality metric suitable for analysis of forensic latent 

fingerprint preprocessing. More precisely, we propose to extend Spectral Image Validation 

and Verification (SIVV) [1] to serve as a metric for latent fingerprint image quality 

measurement. SIVV analysis was originally developed to differentiate ten-print or rolled 

fingerprint images from other non-fingerprint images such as face or iris images. Several 

modifications are required to extend SIVV analysis to the latent space. We implement and 

test this new SIVV-based metric for latent fingerprint image quality and use it to measure the 

performance of the forensic latent fingerprint preprocessing step. Preliminary results show 

that the new metric can provide positive indications of both latent fingerprint image quality 

and the performance of the fingerprint preprocessing.

Keywords:

Forensic latent fingerprint image, Latent fingerprint image quality measurement, Spectral 

Image Validation and Verification Metric (SIVV), Latent fingerprint preprocessing, 

Fingerprint image enhancement.
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1 INTRODUCTION

1.1 FORENSIC LATENT FINGERPRINT

Fingerprints have been used to identify persons for centuries. As one of the most prevalent 

and powerful types of forensic evidence that can be recovered during the investigation of a 

crime, fingerprints have been routinely used for person identification in crime scenes. 

There are different types of fingerprints. Ten-print and rolled fingerprints are captured on a

fingerprint card or by special electronic devices. These fingerprint capture devices generally 

have built-in monitoring systems to guarantee the image quality. By contrast to this 

controlled capture process, the term latent fingerprints, or latents, refer to fingerprint 

impressions that are left unintentionally. Generally latents are partial prints lifted from 

surfaces of objects found at crime scenes that are touched or grasped by a person’s fingers. 

Lifting of latent fingerprints involves complicated processes. Several chemical and physical 

development techniques are available to enhance the visibility of the friction ridge detail

including: photographing the prints under different light sources, dusting with powders, and 

chemical processing. Ideally, the enhanced print can be lifted from the substrate and 

transferred to a secondary backing material that serves to improve the contrast of the ridges 

with respect to the background. While these development techniques improve fingerprint 

features, generally latents are of significantly poorer quality compared to rolled prints. This 

can depend on the substrate of the original latent impression. For example, when attempting 

to lift prints from porous paper substrates such as newsprint, magazines, and wallpaper, the 

lifted fingerprints’ quality may be very low in some cases, and they are not usable for 

recognition even after preprocessing. Moreover, latent fingerprint backgrounds can exhibit

diverse combinations of color, design, and texture that can mask the identity and spatial 

configuration of minutiae in a questioned print. In spite of these drawbacks, latent 

fingerprints are extremely useful in forensics to investigate crime scenes [22][23][24][25]. 

As a widely used biometric, latent fingerprints support an irreplaceable functionality: 

fingerprint recognition. Fingerprint recognition is a technique that can link latents to suspects 

whose fingerprints were previously enrolled in ten-print or rolled fingerprints databases, or to 

link to latent fingerprints from different crime scenes. 

1.2 LATENT FINGERPRINT PREPROCESSING

The performance of a fingerprint recognition system is heavily dependent on the quality of 

the collected fingerprint images. This poses a problem for latent fingerprints as their image 

quality is generally low due to the combination of difficulties in lifting the print from 

substrate and image contamination by complex background noise. As a result, fingerprint 

structure such as minutiae and ridges may not be clearly visible to the human eye of a 

fingerprint examiner, nor easily detected by the algorithms in automatic matching systems.
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Due to the poor quality of latent fingerprint images, digital image preprocessing is a 

necessary step in the forensic analysis workflow [2]. Image preprocessing is performed to 

increase latent fingerprint image quality. Some of the common transformations employed in 

service of this goal include: color management, contrast adjustment, edge enhancement, 

background suppression, and noise filtration [3] [4] [5] [6] [7] [8] . Figure 1 shows an 

example of forensic latent fingerprint image preprocessing: the color image on the left is the 

image directly collected from the crime scene, and the gray-level image on the right is the 

image after preprocessing.

Clearly the fingerprint in the image after processing (on the right) visually exhibits more 

fingerprint pattern information. Figure 2 shows another example in which image 

preprocessing is essential. The image on the left shows a crime scene lift in which an 

impression is left on duct tape. The grid pattern of the duct tape is clearly visible in a green 

hue which, to the eye, completely masks fingerprint information. The image on the right 

shows the same latent print image after preprocessing. In this example, preprocessing 

included: developing using Basic Yellow (dye stain), color filtering using Channel ‘a’ in the 

Photoshop’s Lab Color mode, application of a Fourier-based pattern removal filter (which is 

a product of Foray Technologies), and greyscale management with the Photoshop Levels

command. The end result is a preprocessed image that clearly reveals ridge flow.

Before After

Figure 1: An example of forensic latent fingerprint preprocessing.
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The prevalence of latent fingerprint image preprocessing belies the fact that it is not a single 

activity but rather a complex process containing several sub-varieties. The preprocessing 

workflow is diverse in many aspects including: 1) the aggressiveness of the image 

preprocessing used to suppress various types of noise and, conversely, enhance fingerprint 

information; 2) the software tools that are used; 3) specific algorithms employed; and 4) 

operator-specific preferences. For one, different image software systems use different 

implementations of even the most basic image functions. See, for example, the multiple 

implementations of RGB-to-grey-scale conversation [3] [4]. Even more, the same fingerprint 

may be enhanced in different ways as fingerprint examiners may have their own analysis 

style. Finally, the desired endpoints of the preprocessing may be different, and matching 

systems can have distinct criteria for their input. Figure 3 shows an example of different 

endpoints for the same input image.

The overarching principle for image preprocessing is that the image transformations should 

neither add to, nor subtract from, fingerprint information contained within the image [24].

This guiding principle presently lacks analytical underpinnings. In [22], a crucial question 

was asked by the Scientific Working Group on Friction Ridge Analysis, Study and 

Technology (SWGFAST): “What methods can enhance images without risking bias in 

results (e.g., due to altering image)? (Question 1.3.1 in page 30)” Although there is some 

research on this topic, see for example, the "image processing" paragraph on page 34, and 

"Effects of development techniques" on page 35, universal standards of best practice are 

needed. 

Before After

Figure 2: The necessity of latent fingerprint preprocessing.
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The goal of this study is to characterize the effects of image preprocessing that transforms 

the latent fingerprint image obtained from the crime scene (‘before image’) to the image used 

for identity analysis (‘after image’).

1.3 THE SOFTWARE AND TOOLS FOR LATENT FINGERPRINT 

PREPROCESSING

A number of image analysis tools are capable of revealing latent fingerprint information 

comingled with noisy background features. The range of applicable software includes: 

commercial photo editing tools designed for general purpose image editing such as Adobe 

Photoshop and Pixelmator; open source image editing tools such as the GNU Image 

Manipulation Program and Inkscape; scientific toolboxes such as the Matlab image 

processing toolbox and Open Source Computer Vision; and specialty software particularly 

designed for latent fingerprint analysis such as the FBI's Latent Fingerprint Services, 

Universal Latent Workstation, Automated Fingerprint Identification System (AFIS)’s 

interactive graphical user interface for latent prints, etc. Although there are general common 

functions in those software toolboxes, different software tools are designed for different 

purposes and used for different applications. Specifically for forensic fingerprint recognition, 

we need to define analysis and procedures to verify that the software follows the basic 

principle and requirements of latent fingerprint preprocessing, thereby ensuring the 

repeatability of the process and guaranteeing image integrity.

(a) Original latent fingerprint

Figure 3: Distinct endpoints of forensic latent fingerprint preprocessing.

(c) After first preprocessing (b) After second preprocessing 
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2 METHOD

2.1 LITERATURE REVIEW

Although latent fingerprints are well and widely studied by forensic scientists [3], there has 

been little systematic analysis of forensic latent fingerprint image preprocessing. Digital 

image preprocessing is a relatively new area requiring research in order to establish its 

scientific foundations. It is especially critical to investigate the image transformations used in 

the course of this analysis, and to propose effective approaches or give general suggestions to 

guide the preprocessing workflow to improve the latent fingerprint quality and preserve the 

fingerprint integrity at the same time. The principle objective of this work is to initiate such 

investigations.

Latent fingerprint images are obtained under non-ideal acquisition conditions; the finger 

impression may be incomplete, distorted, or corrupted by background noise. In most cases, 

the latent fingerprint quality is crucial for latent identification. The research community has 

developed several approaches and algorithms on fingerprint image quality [11], [12], [14], 

[15] and latent fingerprint preprocessing [13]. In [14], a latent fingerprint image quality 

(LFIQ) measurement was proposed. In [13], Yoon, et al. proposed a latent fingerprint 

enhancement algorithm requiring a manually marked region of interest (ROI) and singular 

points. The paper proposed a novel orientation field estimation algorithm, which fits the 

coarse orientation map to an orientation field model. Experimental results on the NIST SD27 

Latent Fingerprint Database [19] indicate that, with the use of the proposed enhancement 

algorithm, the matching accuracy of the commercial matcher, Rank-m identification Rate, 

was improved by 5% to 20%.

2.2 METHOD OVERVIEW

We seek to compare the image qualities of before images ˗ original RGB images, directly 

obtained from forensic crime scene photography ˗ and after images ˗ the grey scale image 

after preprocessing ˗ to evaluate the performance of the preprocessing effect. 

In the context of latent fingerprint analysis, a primary objective is to improve contrast 

between ridges and furrows, thereby enabling clearer identification of minutia points. The 

ridges and furrows appear as periodic structures in the fingerprint image. This periodicity 

manifests as narrow regions with relatively high-energy content in the frequency spectrum of 

the image. It is natural to construct a latent fingerprint quality metric built on this feature.

Our latent fingerprint quality metric is based on an extension of the Spectral Image 

Verification and Validation analysis (SIVV) [1]. The original SIVV algorithm was designed 
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for image validation and verification of ten-print fingerprint images from live-scan devices, 

and for maintaining the fidelity of fingerprint image databases. SIVV can effectively 

differentiate the non-fingerprint input from the flat or rolled fingerprint input. As the periodic 

structure of the fingerprint ridges and furrows is a level one feature, SIVV is potentially 

applicable to the latent fingerprint preprocessing domain. However, latent fingerprint images 

are often corrupted by complex background noise, and the ridge structures may not be clearly 

visible. Furthermore, latent images are generally of poor quality and the fingerprints can be 

incomplete. These characteristics will confound the original SIVV feature analysis; 

refinement is needed for SIVV to be applicable to latent fingerprint images. 

We implement several modifications to SIVV in light of the above-mentioned difficulties. In 

order to suppress confounding background noise, in the spatial domain the algorithm focuses 

analysis on a region of interest within the fingerprint image. Furthermore in the frequency 

domain, the algorithm constrains the SIVV peak to be within a limited range, which can be 

inferred by the fingerprint ridges’ pixel distances on the latent fingerprint images. The 

resulting metric is still based on the intrinsic Fourier spectral properties of latent fingerprint 

images. The new latent fingerprint quality metric provides the quantitative measurement to 

characterize the quality of the latent fingerprint images and measures the effectiveness of the 

latent fingerprint preprocessing process. 

2.3 SIVV ON FLAT/TEN-PRINT OR ROLLED FINGERPRINT

SIVV analysis derives from the periodicity of ridges and furrows [1]. For completeness, first 

we summarize the original SIVV algorithm (for the detailed presentation, please refer to the 

original report [1]). 

Step 1. Image Windowing

The standard one-dimensional Blackman window is given in the following equation:

w(n) = 0.42 − 0.5 cos (

2πn

N − 1

) + 0.08 cos (

4πn

N − 1

) (1)

where 0 ≤ n ≤ N − 1

The length of the one-dimensional window is N. The constant numbers in the equation are 

the same as the standard Blackman filter. Given the image with N rows and M columns, the 

two-dimensional Blackman Window is the tensor product of windows of length N and M. 

When the 2D Blackman Window is applied to the fingerprint image, the window is applied 

on the center of the fingerprint texture, and the size is adapted to the size of the fingerprint 

image. 
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Step 2. Discrete Fourier Transform (DFT)

H(u, v) = ∑

M−1

x=0

∑ exp [2πiy

v

N

] exp [2πix

u

M

] h(x, y)

N−1

y=0

(2)

Here u and v denote frequency components in the x and y directions ranging from −M

2

to 

M

2

and −N

2

to 

N

2

respectively. 

Step 3. 2D (normalized) Log Power Spectrum

The 2D power spectrum is computed as:

P(u, v) = |H(u, v)|

2 (3)

Depending on the implementation, the output of this step can be normalized or notnormalized; that is

10 ∗ logP(u, v) (4)

Or 

10 ∗ log

P(u, v)

P(0,0)

(5)

Step 4. 2D Polar Transform of Power Spectrum

The 2D power spectrum is represented in polar coordinates using the transformation:

ρ =

√u

2 + v2

√M2 + N2

(6)

θ = tan−1(

v

u

) (7)

where the [0, 0] point is in the image center. We use P(ρ, θ) to represent the 2D results of 

the polar transformation, where ρ is divided by the maximum dimension of the input image 

N, normalized to 0 and 0.5 cycles/pixel.

Step 5. 1D Normalized Polar Transform

Finally, the 1D polar transform is computed as the sum over angles of:
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P(ρ) = ∑P(ρ, θ)

180

θ=0

ρ = 0, ... ,0.5 cycles/pixel

(8)

The normalized 1D polar curve is:

PN(ρ) =

P(ρ)

P(0)

ρ = 0, ... ,0.5 cycles/pixel

(9)

DFT

Polar 

Transform

10*log(P) or 

10*log(P/P0) 

2D power 

spectrum

2D polar 

spectrum

Blackman 

Window

Input 

image

 θP (ρ, θ) 

/ θP (0, θ)

1D polar 

spectrum

Filtered 

image

Normalized 2D log 

power spectrum

Figure 4: SIVV algorithm. 
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Figure 4 shows the algorithm schematic. Figure 5 shows the analysis step-by-step.1 The 

clear peak and valley in the polar power spectrum (sub-figure (g)) is indicative of the ridgeflow periodicity and is referred to in the following as the SIVV feature.

The original objective of SIVV analysis is to screen fingerprint image databases for 

specimens improperly scanned from fingerprint cards [1]. In the auto-capture process, SIVV 

analysis can help to identify the auto-capture failures, identify non-fingerprint images that 

may have been incorrectly included in a fingerprint database, etc. The original SIVV 

algorithm focuses on the fingerprint datasets that were captured under controlled 

environments, such as flat/ten-print or rolled fingerprint database, or mixed database which

contains face, iris and fingerprint, etc. Generally, the fingerprints in such datasets have clean 

background and significant less noise compared with the latent fingerprint image dataset.

 

1 Courtesy of SIVV software package in NBIS [1] [20], the original image is G001T2U.tif in [19].

(e) 2D power spectrum

(f) 2D polar spectrum (g) 1D polar spectrum

(c) After Blackman window (b) Cropped image (a) Original image

dB

Cycles/pixel

Figure 5: An example of SIVV feature on a rolled fingerprint image. 
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2.4 MOTIVATION OF OUR APPROACH

The original SIVV feature performs well on the flat/ten-print or rolled fingerprint database, 

which are captured by inking methods or live-scan devices in an attended mode. In such 

contexts, background noise is minimized during the capture, and the contrast between the 

ridges and furrows is relatively high. As image quality is generally controlled very well, the 

fingerprint image ridges and valleys are clear and computer-readable. In such cases, the 

periodic structure of the ridges and valleys can be captured by Fourier spectrum analysis in 

the frequency domain. It follows that SIVV performs well when the original fingerprint 

image is of good quality.

In comparison with rolled fingerprints, original latent fingerprint images are of much poorer 

quality in several respects. Latent fingerprints are generally smudgy and blurred. They often 

capture only a small finger area, and may have large nonlinear distortion due to pressure 

variations. Finally, and perhaps the most damaging source of noise from the perspective of 

SIVV analysis, in latent fingerprint images, it is not uncommon for the fingerprint image to 

be superimposed on a structured background. This background “noise” is unavoidable and is 

extremely hard to model due to the large variety of background colors, textures, etc. When 

background noise is strong, the spectral spike from the fingerprint periodicity can be 

conflated with the signals of other periodic structures of the images. Sources of such 

patterned noises are diverse and include textile fabric, written text, and residue from the 

(a) Original image (b) Cropped image (c) After Blackman window

(d) 2D power spectrum (e) 2D log polar spectrum (f) 1D log polar spectrum

Figure 6: An example of the SIVV feature on a latent fingerprint image.
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physical processing. Figure 6 shows an example2 of original SIVV on a latent fingerprint 

image in the NIST Database 27 [19]. It shows that the SIVV spike is largely weakened due to 

the fingerprint incompleteness and from being submerged in the background noise.

Figure 7 (implementation courtesy of [20]) shows the results of applying the original SIVV 

analysis [20] to six unprocessed, latent fingerprint images taken from a database of training 

images provided by Schwarz Forensics and Foray Technologies (the image database is

described in more detail in 3.1). These images demonstrate that the original SIVV analysis 

may not be directly applicable to latent fingerprint images. In the cases 1, 3, 5, and 6, there is 

no obvious spike (fingerprint level-one information) at all. In cases 2 and 4, the detected 

peak (red arrow) does not represent the fingerprint ridges and furrows, but rather arises due 

to texture of the background. In addition, 1-c and 2-c show the detailed texture noise (tiny 

grids) on the images. The actual fingerprint ridge peak is shown by the green arrows in 2-b

and 4-b. 

In this report, we introduce two additional components to the SIVV analysis to regain the 

valuable metrical functionality that is otherwise lost in latent fingerprint contexts. First, we 

introduce a Region of Interest (ROI) selection to enable the analysis to focus only on a local 

subregion which contains fingerprint signal in spatial domain. Second, we introduce a

constraint in the spectral analysis restricting attention to a small window which may contain 

the ridge and furrow spike in the frequency domain.

 

2 Courtesy of SIVV software package in NBIS [1] [20], the original image is G001L2U.tif in [19].
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2.5 BLACKMAN WINDOW

In fingerprint analysis, the Blackman window is used to suppress signal outside of the 

fingerprint region in addition to eliminating non-periodic boundary effects. The standard 1D 

Blackman Window is defined by:

w(n) =

1 − α

2

−

1

2

cos (

2πn

N − 1

) +

α

2

cos (

4πn

N − 1

)

(10)

1-a. Original image (158) 1-b. 1D log polar spectrum

2-a. Original image (165) 2-b. 1D log polar spectrum

1-c. Zoom in the background noise of (2-a)

2-c. Zoom in the background noise of (3-a)

3-a. Original image 3-b. 1D log polar spectrum 4-a. Original image 4-b. 1D log polar spectrum

5-a. Original image (160) 5-b. 1D log polar spectrum 6-a. Original image 6-b. 1D log polar spectrum

Figure 7: Examples of the SIVV feature on latent fingerprint images.
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where 0 ≤ n ≤ N − 1 and α = 0.16. The resulting curve is shown in (Figure 8). The 2D 

Blackman window filter is the cross product of two 1D Blackman window vectors (N maybe 

different in two 1D vectors).

 

In latent images, variability in fingerprint location, orientation, and size requires that we add 

more flexibility in application of the Blackman window filter to the image. We include 

additional parameters to control the location (center point of the 2D filter), size (if the 

fingerprint region is modeled by an ellipse with the ellipse’s major axis of Nmax pixels and 

minor axis of Nmin pixels respectively, the size can be controlled by using Nmax and Nmin 

in the Blackman window equation), and orientation (the filter can be rotated in 2D plane) of 

the Blackman window. The constant numbers in the Blackman equation control the shape of 

the filter. To be faithful to the original Blackman filter, we keep the constant number 

unchanged and the curve shape unchanged. In practice, in order to increase the SIVV signal 

strength, we like to focus only on the ridge furrow patterns by customizing the size of the 

filter window to the size of the fingerprint area, and aligning the Blackman filter center to the 

fingerprint center. Figure 9 contrasts the following SIVV results: no Blackman window (a);

original Blackman window (b); and customized Blackman window (by customizing the size, 

aligning the location and orientation) (c). We see that the regular Blackman window does 

help to strengthen the fingerprint signal in some cases. The customized Blackman Window 

has more flexibility to select the best region, which is very useful to latent fingerprint image 

analysis as location, orientation, and extent of the print region vary greatly in latent 

fingerprint images.

Figure 8: Blackman Window (α = 0.16). 
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(a) a-1. Original image without Blackman Window (051e)

(b) b-1.Blackman Window b-2. Blackman window on whole image b-3. SIVV on b-2

(c) c-1. Customized Blackman Window c-2. Image after customized Blackman Window c-3. SIVV on c-2

(d) d-1. ROI image [698 62 1519 589] d-2. Blackman window on ROI image d-3. SIVV on d-2

Figure 9: The comparison of SIVV curves with different input options.

a-2.SIVV on whole image
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2.6 REGION OF INTEREST (ROI)

Latent fingerprint images often include significant noise (where noise is defined as any 

image feature that is not clearly identifiable as fingerprint information). Noise that overlaps 

with fingerprint region is intrinsically harder to diminish while simultaneously maintaining 

fingerprint integrity. One exception to this is when the noise is well separated from 

fingerprint information in a color space, in which case color filtration can be a powerful and 

effective tool. In general, such separation is not the case. For this reason, it is important that

our analysis be designed with a high degree of specificity to fingerprint information. By 

contrast, it is relatively easy to remove the background noise in the area outside of the 

fingerprint region. Furthermore, it is important to do so as these regions contain no 

fingerprint information yet, due to the nonlocal nature of Fourier-based spectral analysis, 

they may contribute a large amount of non-signal energy, thereby masking the fingerprint 

SIVV feature in the frequency domain. Thus we specify region of interest (ROI) by selecting 

a rectangular region containing the entire fingerprint. An example is shown in Figure 10. 

Image features outside the ROI are masked by setting the intensity value to a constant black. 

This ROI selection is done prior to the FFT step of the SIVV and performed “in place,” i.e.,

the image remains the size of the original, so as to maintain pixel density and not introduce 

rescaling artifacts. During the latent fingerprint image quality measurement process, there is 

a trade-off between the fingerprint region size and fingerprint region purity. The ROI should 

contain most of the area3 of the fingerprint image with good ridge information. Currently the

 

3 Notice that the ROI proposed here is only for latent fingerprint detection and quality measurement, not for 

latent fingerprint identification. The whole fingerprint region should be considered in the identification 

process. 

Figure 10: The selection of Region of Interest.
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rectangle shape ROI selection must be done manually. In the future, we may consider a 

semiautomatic ROI extraction method perhaps based on an elliptical or polygon region.

Figure 11 shows the comparison of SIVV curves using the whole image and the ROI. The 

SIVV spike (indicated with arrow) of the ROI image is much stronger than the SIVV spike 

of the whole image. The example shows that focusing on the ROI helps to recover the SIVV 

feature from the background noise.

The signal strength of the SIVV peak is predominantly determined by two factors. The first

is the frequency power of the finger print ridge and furrow structure. This power is related to

the area size of the fingerprint region; that is, the larger area includes more ridges and 

furrows and thus the stronger frequency power. The second factor is the signal-tobackground noise ratio (SNR). Here also, the larger the SNR, the stronger the SIVV signal 

peak. In summary, when determining the actual fingerprint ROI image, one must implement 

a trade-off between the size of the fingerprint region and the signal/noise ratio inside this 

region. Choose too small a region and the SIVV signal will be weak; choose too large a 

region such that it includes background noise and the SIVV signal will be buried. Figure 11

demonstrates that if we use full image as input, the fingerprint signal is submerged in the 

background noise. However, with appropriate selection of ROI, the fingerprint signal is 

detected in the SIVV curve. 

Full Image

Region of Interest

Figure 11: An example of Region of Interest.
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2.7 PEAK LOCATION CONSTRAINT

Research has demonstrated that frequency-based filtering can be an effective way to suppress 

background interferences associated with fingerprint evidence [14], [17]. In the SIVV 

computation, a 2D-Fast Fourier transform (FFT) is computed to extract the frequency 

information associated with an image. The 2D Fourier spectrum represents both power 

spectral density and phase information. Under favorable circumstances, the frequencies 

associated with the friction ridge detail in the print will be separable from those frequencies 

associated with the interfering background features. Selective filtering of frequencies 

associated with fingerprint information may filter out the background interference and

correctly locate the SIVV peak. 

The distance between fingerprint ridges is generally distributed over a narrow range. The 

study of [18] reports that the distance between ridges “ranged from 0.2 mm to 0.85 mm on 

fingerprints of male subjects and from 0.2 mm to 0.75 mm on fingerprints from female 

subjects. The mean ridge-to-ridge distance for 731 measurements on the male subjects was 

0.46 mm. In 1046 measurements on the female subjects, the mean value was 0.41 mm.” If 

the image resolution of the fingerprint image is known, then the distances between ridges

measured in pixels may be estimated. The location of the SIVV fingerprint peak directly 

related to the repetitive ridge pattern and the ridge distance in pixels, can furthermore be

calculated from the physical ridge distance range and the image resolution. See Figure 14 in 

NIST report [1], which shows the distribution of frequency location of SIVV feature for nonfingerprint image and fingerprint image in a mixed image dataset. It demonstrates the 

concept: for fingerprint images with 500 ppi resolution, most of the SIVV peak locations are

between 0.01 to 0.15 cycles per pixel. Given 0.01 cycles per pixel is equal to 0.01×500/25.4 

cycles per millimeter, which is about 0.197 cycles per millimeter (where 1 inch = 25.4 

millimeter, the image resolution is 500 pixels per inch). 0.01 to 0.15 cycles per pixel is 

equivalent to 0.197 to 2.95 cycles per millimeter; the mean of the peak location is around 

0.08 cycles per pixel (about 1.57 cycles per millimeter). The same results are also shown in 

Appendix C, Figure C-5 and C-6 in the NIST report [1]. The two figures show the 

distributions of frequency location of SIVV peak for SD27 and SD29 dataset sampled at 500 

ppi respectively. The peak locations consistently fall in the similar range between 0.01 to 

0.15 cycles per pixel (0.197 to 2.95 cycle per millimeter) and the mean of the peak location is 

also around 0.08 cycles per pixel (1.57 cycles per millimeter). More precisely, the peak range 

for the SD27 and SD29 datasets concentrates between 0.05 to 0.12 cycles per pixel (about 

0.98 to 2.36 cycle per millimeter), and the mean of the peak location is around 0.08 to 0.09 

cycles per pixel (about 1.57 to 1.77 cycles per millimeter). Figure C-4 in [1] also shows the 

similar concept for fingerprint images in SD27 with 1000 ppi resolution: the peak location 

range is between 0.025 to 0.06 cycles per pixel, which is also about 0.98 to 2.36 cycles per 

millimeter, and the mean is around 0.04 cycles per pixel (which is also about 1.57 cycles per 

millimeter). The range value in cycles/mm in Figure 14 of NIST report [1], Figure C-4, C-5, 

and C-6 in [1] are consistent (0.197~2.95 cycles per millimeter) despite the image resolution.
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The peak location can be estimated given the image resolution and the range of ridge 

distance. In practice, if there is a peak that is well outside this range, one may assume that the 

feature is generated by the background texture instead of the fingerprint (as shown by 

example 2 and 4 in Figure 7). In this manner, one can selectively filter out the background 

interference to remove the fake peaks and correctly locate the SIVV peak.

Figure 12 shows an example where the original image SIVV spectrum is shown in the first 

row, and the enhanced image spectrum is shown in the second row. The strongest peak in the

original image is around 0.15 cycles per pixel (about 7.09 cycles per millimeter) in 1200 ppi 

images (the red arrow in Figure 12-3). By zooming in and looking closely at the details on 

the image in Figure 12-2, we can clearly see the grid texture in the background. The strongest

peak around 7.09 cycles per millimeter is not a fingerprint peak but rather is derived from the

frequency of the background texture, whose peak location is much greater than the 

fingerprint peak’s upper bound (2.95 cycles per millimeter). The actual SIVV peak is the 

weak peak in the blue bar (the green arrow in Figure 12-3), which is around 0.035 cycles per 

pixel (0.689 cycles per millimeter) and in the range of fingerprint peak location (0.197 to 

2.95 cycles per millimeter). The second row in Figure 12 confirms the analysis. After 

preprocessing, the fingerprint signal is strengthened and the background noise is weakened. 

In this case, the SIVV peak located inside the predicted bar is the strongest peak while the 

background texture’s peak is weakened and becomes smaller. In summary, we can define the

peak location constraint; that is, the approximate range for SIVV peak of the fingerprint with 

Figure 12: An example of SIVV peak location constraint.

1. Original (153)

4. Enhanced (153)

2. Zoom in 

5. Zoom in on enhanced image

3. SIVV of original image

6. SIVV of the enhanced image

3. ROI

7. ROI
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1200 ppi resolution is 0.197 to 2.95 cycles per millimeter, which is 0.004 to 0.062 cycles per 

pixel4 and indicated by the blue bar in Figure 12.

2.8 SOME PROPERTIES OF THE SIVV

We draw attention to the invariance of the SIVV feature with respect to image noise and a 

common preprocessing image transformation. More exhaustive analysis of the propagation 

of SIVV analysis through a wider variety of preprocessing transformations and image noise 

models is a topic for future research.

2.8.1 Image inversion

Latent fingerprint examiners generally prefer that fingerprint information appear as black 

ridges as our eyes are better at picking out slightly off-white colors on a white background

than distinguishing less black pixels on a black background. Figure 13 shows the image 

developed by white powder techniques and its invert image. It shows that normally human 

eyes are more sensitive to the black ridges in white background. Thus, for the latent 

fingerprint images which are developed by white powder techniques, generally it is preferred 

to invert the image to change it to black ridges with a white background. The operation of 

invert is:

Iinvert = 255− Ioriginal for all pixels in Ioriginal (11)

 

4 0.197 cycles per millimeter = 0.197 × 25.4/1200 cycles per pixel = 0.004 cycles per pixel. 
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Although the image and its invert are quite different to human eyes, the invert operation does

not add or remove any information. It is just a different way to represent the information. 

Thus, if we measure the fingerprint using the machine algorithms, both cases may provide 

the similar results except that the invert operator adds a power spectrum with a peak in the 

center (0,0) and zero otherwise. In our experiments, we found that the metric curve of the 

invert image is very similar to the metric curve of its original image. Figure 14 shows an 

example. The SIVV curve nicely preserves the fingerprint ridges information in the invert 

image cases. 

1. Original image

6. Invert image

Figure 13: The original image and its invert image of white power developed prints.

11. Red: original, Blue: Invert 

2. Blackman window 

on original image

3. Power spectrum of 

original image

4. Polar spectrum of 

original image

7. Blackman window 

on original image

8. Power spectrum of 

original image

9. Polar spectrum of 

original image

10. SIVV of invert image

5. SIVV of original image
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We can estimate the difference of the metric curves between the original image and its invert 

image in a mathematical way. Following the definition of SIVV given in Section 2.1 without 

considering the Blackman window filter (which is an optional step), according to the next 

four steps of SIVV computation, we have the relationship of the intermediate results of the 

original image and the invert image in each step:

(1) According to the linearity property of the DFT, we have:

D[Iinvert(x, y)] = D[255 − Ioriginal(x, y)] = D[255 ∗ ones(M, N)]− D[Ioriginal(x, y)]

= A matrix with a value in (0,0), and 0 for others − D[Ioriginal(x, y)]

(12)

1. Original image 2. Blackman window 

on original image

3. Power spectrum of 

original image

4. Polar spectrum of 

original image

7. Blackman window 

on original image

8. Power spectrum of 

original image

9. Polar spectrum of 

original image

6. Invert image

Figure 14: The comparison of SIVV curves of an image and its invert image. 

5. SIVV of original image 10. SIVV of invert image 11. Red: original, Blue: Invert
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(2) The 2D power spectrum is the magnitude of the DFT results. Without normalization 

on the signal, the original and invert image’s 2D power spectrum is the same except 

in the origin point. 

(3) The 2D polar spectrum is the coordinate transformation of the 2D power spectrum.

(4) The 1D polar is the sum in θ direction, and normalized by Po

(ρ).

Thus, assuming that the ROI and Blackman Window are unchanged, we observe that the

SIVV curves of the original image and its inversion are identical except at the origin point. 

After normalization and taking the logarithm, this difference manifests as an elementary shift

of the SIVV curve up or down.

2.8.2 SIVV on noise images

We also test the SIVV analysis for robustness to independent identically distributed (IID) 

random noise. The Matlab function, R = randi(IMAX,N), is used to generate the 

pseudorandom integers from a uniformed discrete distribution, and it returns a N by N matrix 

containing pseudorandom integer values drawn from the discrete uniform distribution on 

[−IMAX: IMAX]. The Matlab codes for image generation are:

...

% I is the input image, magnitude is the noise level, and I_Noise is the output image.

r = randi([(-1)* IMAX, IMAX], size(I) )

I_Noise = double(I) + r;

I_Noise = uint8(I_Noise);

...

Note, in the above Matlab code, uint8(X) converts the elements of the array X (here, X is 

I_Noise) into unsigned 8-bit integers. X can be any numeric object, such as a DOUBLE. The 

values of a uint8 range from 0 to 255. Values outside this range saturate on overflow, namely 

they are mapped to 0 or 255 if they are outside the range.

We performed an experiment on the comparison of SIVV curves of an image with different 

levels of IID random noise without considering the Blackman window filter (which is an 

optional step). Figure 15 shows the initial image and the images by adding different random 

noise on it. The pictures shown are blurred due to Microsoft Word image compression. To 

1. Original image 2. IMAX = 3 3. IMAX = 10 4. IMAX = 50 5. IMAX = 100

Figure 15: The image with different levels of IID random noise.
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the eye, at the resolution shown, the images in Figure 15 may appear identical. The highfrequency noise implied by our IID noise model can potentially affect the analysis. Figure 

16 shows a portion of the IID random noise matrix in the experiment that added noise to the 

original image. Figure 17 shows the results of the SIVV curves by adding different levels of 

noise to the same image. It shows that the peak locations’ x coordinates are the same and the 

SIVV peaks’ shapes are very similar. The SIVV curves are similar but the tails vertically 

move up along the Y axis as the noise magnitude increases. If we add more noise, the tail is 

even higher.

1. IMAX = 3

2. IMAX = 10

Figure 16: The image matrices with random noise.
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3 EXPERIMENTAL RESULTS

3.1 LATENT FINGERPRINT PREPROCESSING DATASET

As a preliminary means to demonstrate objective assessment of latent fingerprint 

enhancement, we compared the SIVV characteristics of fingerprint images pre- and postenhancement. A training dataset was provided for our study. In the dataset, there are six 

types of latent fingerprint images: Bi-Chromatic mag powder developed prints, Bi-Chromatic 

powder developed prints, black ink pad on colored background, Ninhydrin developed prints, 

silver mag powder developed prints, and white powder developed prints as shown in Figure 

18. We use 39 forensic latent fingerprint image pairs in our experiment. The number of pairs

in the six types is not balanced. For each image pair, the before image is an RGB color image 

which was scanned by a high-resolution flatbed scanner. A Certified Latent Print Examiner 

(CLPE) preprocessed the before image within Adobe Photoshop (which is the primary image 

analysis tool used by CLPE’s practicing today), converted it to grey image, and saved it as 

tiff format as the after image. The before and after image pairs are both in the same size, 

resolution, and saved in tiff format. Each image contains at least one latent fingerprint. The 

background for some images is very noisy. The fingerprint ridges and furrows are in low 

contrast and very blurry. Some images only contain a partial fingerprint image (less than 1⁄4 

fingerprint).

1. SIVV of the original image 2. magnitude = 3 3. magnitude = 10 4. magnitude = 50 5. magnitude = 100

Figure 17: The comparison of SIVV curves of an image with different levels of IID random noise. 
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NISTIR 8017 Page 25 7/2014

Figure 18: The latent fingerprint preprocessing image pairs from a training course (six types).

(5) Silver mag powder 

developed prints

(6) White powder developed 

prints

(1) Bi-Chromatic Mag Powder 

Developed Prints

(2) Bi-Chromatic Powder 

Developed Prints

(3) Black ink pad on colored 

background

(4) Ninhydrin developed prints
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3.2 LATENT FINGERPRINT QUALITY MEASUREMENT

Our SIVV algorithm including modifications for latent fingerprint image quality 

measurement is diagrammed in Figure 19. The blue color represents original implementation. 

The green color represents new components. In the proposed algorithm implementation, the 

option of a manual ROI selection GUI interface is added, so that the user will interactively 

select the region of the fingerprint. The new algorithm also revised the peak location 

detection module in order to select the correct peak using a peak location constraint.

3.2.1 Region of interest

For some images in our dataset (Figure 7, cases 2 and 4), the background noise also includes 

a strong repeated texture pattern, which provides a strong noise peak in the SIVV curve. 

Instead of representing the fingerprint ridge information, this peak represents the background 

texture pattern. In order to reduce the background noise, we need to select the fingerprint 

region and calculate the SIVV curve only on ROI image, which contains strong fingerprint 

level-one features instead of background noise. 

Figure 19: The proposed algorithm for latent fingerprint quality measurement.

Original Auto center

Image

ROI

[xmin, xmax, ymin, ymax]

whole

Image 

region

Blackman 

Window

SIVV

SIVV 

graph

Manually select 

region

Peak selection 

based on 

constraints

Peak location 

constraints

Candidate Peak 

detection

Peak detection 

result
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Due to the poor quality of the latent fingerprints, automatic ROI extraction is a very 

challenging problem. In the present experiment, we implemented a graphical user interface to 

manually select rectangular ROI on the displayed input image. In the future, we may explore 

the use of a polygon to enhance the accuracy. In addition, we may also propose a 

semiautomatic ROI extraction method where, given the center of the fingerprint region and 

the radius of an ellipse which roughly covers the ROI, the algorithm automatically finds the 

maximum of the strongest SIVV signal peak and locates the accurate boundary of the ellipse.

Figure 20 shows that when the SIVV algorithm takes the whole image as input, it detects the 

wrong peak which actually represents the texture noise, while if we use a manually selected 

ROI as input for the analysis, the algorithm correctly detects the fingerprint peak.

3.2.2 Peak location constraint

Within the ROI, we implemented an additional interface that allows the user to draw line 

segments from one ridge perpendicular to itself to another adjacent ridge. The user can draw 

multiple segments including some in both dense and sparse ridge areas. These fiducial 

lengths serve as benchmark estimates of the ridge-to-ridge distance measured in pixels 

which, in turn, helps to anchor the spectral search region for the SIVV analysis. As long as 

the background noise’s spectrum is smooth throughout the frequencies containing fingerprint 

ridge information, the SIVV algorithm will identify the corresponding peak range in the

specified and limited frequency domain. 

1. Original image

2. SIVV of original image

2. ROI 4. SIVV of ROI

Wrong peak detected

Correct peak detected

Figure 20: The comparison of whole image vs. ROI image. 
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Figure 21 shows an example of using a location constraint to find the correct fingerprint 

peak. The strongest peak is around the location at 0.15 cycles per pixel. Zooming in on the 

image in Figure 21-3 reveals a pervasive uniform grid texture in the image background. 

From elementary analysis, we may conclude that the strong peak around 0.15 is actually not 

the fingerprint peak, but rather represents the frequency of the background texture.

According to multiple estimates of the fingerprint ridge distance in the image, the possible 

spatial frequency range for the fingerprint SIVV peak is indicated by the blue bar. The actual 

SIVV peak is the weak peak in the blue bar around 0.03 cycles per pixel. The image 

analyzed in Figure 21 is an example “before image” from our dataset. Figure 22 shows the 

image obtained after preprocessing. The combination of ROI selection and marking interridge distances diminishes the spectral peak due to the background texture and allows for 

automatic detection of the SIVV feature in the polar spectrum plot.

1. Original image

2. ROI

4. SIVV peak (fingerprint)

3. Zoom in to see the background noise texture

Figure 21: Peak location constraint.
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Figure 23 shows the correct peaks are detected using peak location constraints in different 

latent fingerprint images. 

1. Original image (037 enhanced) 2. Selected ROI 3. Specify ridge distances (max, min)

4. SIVV result

Figure 22: Proposed procedure for latent fingerprint SIVV feature detection.
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3.3 COMPARISON RESULTS

We performed five experiments on our latent fingerprint preprocessing dataset to 

demonstrate the efficacy of the SIVV modifications described above in concert with latent 

fingerprint image preprocessing. If the correct SIVV peak is detected, we count it as true 

positive. We calculate the true positive rate (TPR): TPR = True Positive (TP) / (True Positive 

+ False Negative). The five experiments are:

(a) The SIVV implementation provided by the NBIS software package [35], using the 

default option (all parameters are set to default values). In this setting, the algorithm 

cuts the image first, then applies the Blackman filter, and calculates SIVV curve. 

(b) The SIVV implementation provided by NBIS, using the whole image option (all 

other parameters are set to default values).

(c) The SIVV implementation provided by NBIS, with a resized input image (half of 

original width and height) (all other parameter are set to default values). In this 

setting, the algorithm cuts the image first (different region from experiment a), then 

applies the Blackman filter, and calculates SIVV curve.

(d) A modified SIVV analysis including manual selection of ROI using a GUI interaction 

(all other parameters are set to default values).

(e) A modified SIVV analysis including both manual selection of ROI and manual input 

of the ridge distances by line segments using a GUI (all other parameters are set to

default values).

Figure 23: The frequency feature of latent fingerprint using the proposed algorithm.

dB

Cycles/pixel

dB

Cycles/pixel

dB

Cycles/pixel
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The results are shown in Table 1. Clearly the successful detection rates of the preprocessed

images (the values in the second row) are consistently higher than the unprocessed

counterparts (the values in the first row). Based on this, we conclude that preprocessing can 

be useful in amplifying fingerprint information contained in latent fingerprint images.

Table 1: The comparison of different algorithms with different options

TPR = TP/(TP+FN)

Original 

image

Default 

option

Original 

image

Whole option

Resize image

Default 

option

Original 

image

GUI ROI

GUI ROI

Peak loc. 

Constraint

Before 36% 33% 62% 79% 85% 

After 64% 72% 82% 87% 92% 

4 DISCUSSION AND FUTURE WORK

The Spectral Image Validation/Verification (SIVV) analysis was introduced to screen 

fingerprint image databases for low-quality and/or non-fingerprint images. It was observed in 

that, “The magnitude of the distinctive spectral feature, related directly to the distinctness of 

the level 1 ridge flow, provides a primary diagnostic indicator of the presence of a fingerprint 

image,” [1]. While effective for controlled capture fingerprints, the diagnostic capability of 

SIVV is significantly impaired in the context of low-quality latent fingerprint images. We 

have introduced modifications to SIVV to restore this capability. Furthermore, we 

demonstrate that the modified SIVV analysis can be used as a quality indicator for 

preprocessing, and that it is an essential precursor to detailed forensic investigation of latent 

fingerprint images.
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NISTIR 8017 Page 32 7/2014

The systematic investigation of forensic latent fingerprint preprocessing is still in an early 

stage and much work remains to be done so as to place this critical component of forensic 

analysis on firmer quantitative foundations. The current SIVV analysis inspects the log polar 

power spectrum for a characteristic peak-trough feature indicative of the ridge periodicity 

(averaged over angles) exhibited by a fingerprint. The possibility exists that this spectral 

feature could be further refined by computing peak-height and/or area between two SIVV 

curves after suitable normalization and alignment. For example, if we define the peak height, 

h, as the height between the two points: the peak (the local highest point) and the first valley 

before the peak (the left local lowest point), the relative differences of the peak height is Δh 

= hafter-hbefore, as shown in Figure 24. If we define the peak width, w, as the horizontal 

distance between those two points, the relative differences of the peak width is Δw = wafterwbefore, as shown in Figure 24 (which is approximately the half of the total hump width). 

Because the end point of the hump is very ambiguous, we define it to be the intersection of 

the curve and the vertical line which is the same w distance from the peak on the right. We 

can also define the peak area and its difference, Δs = safter -sbefore. In doing this, a finer 

quantitative understanding of the quality of fingerprint information contained in an image 

might be developed. We hope to report on such investigations in the future.

Figure 24: Quantitative comparison of latent fingerprint image quality.

The first valley 

before the peak

Peak: 

[x_peak, y_peak]

[x_peak + w, 

y_intersection]
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5 ACKNOWLEDGEMENTS

The authors thank John M. Libert, John Grantham, and Shahram Orandi of NIST for their 

valuable contributions to this work. The authors thank Mathew Schwarz of Schwarz 

Forensics and David Witzke of Foray Technologies for valuable consultation on this work. 

This research was supported by the 2012 NIST Forensic Measurement Challenges grant, 

“Metrics for Manipulation and Enhancement of Forensic Images.”

6 DISCLAIMER

Any mention of commercial products or reference to commercial organizations in this report 

is for information only; it does not imply recommendation or endorsement by NIST nor does 

it imply that the products mentioned are necessarily the best available for the purpose.

7 REFERENCES

[1] John M. Libert, John Grantham, and Shahram Orandi, “A 1D spectral image 

validation/verification metric for fingerprints,” NISTIR 7599, 2009.

[2] Edward M. Robinson, second edition, Crime scene photography. Chapter 10, Digital 

Imaging Technologies, contributions by David “Ski” Witzke, Academic Press, 2010.

[3] Andrew M. Dienstfrey, et al., “Analysis of Image Enhancement Operations Applied to 

Latent Fingerprints,” NIST IR, Gaithersburg, MD, 2014.

[4] Peter Bajcsy, Joe Chalfoun, David A. Nimorwicz, and Andrew M. Dienstfrey, 

“Estimation of Mathematical Models Relating Latent Fingerprint Images Before and 

After Enhancement,” NIST IR, Gaithersburg, MD, 2013.

[5] Mary Theofanos, Brian Stanton, et al., “Characterizing the Latent Fingerprint Preprocessing Procedures,” NIST IR, Gaithersburg, MD, 2014.

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APPENDIX A: THE SIVV PEAK ON SYNTHETIC IMAGES 

In order to study the properties of SIVV and find out the relationship between the SIVV peak 

location and texture frequencies, we generated a set of synthetic images. The first image set 

is a set of black/white stripe images with different stripe width. The size of the images in the 

first image set is 512 by 512. For each image, given the width of the stripes, we generate the 

image with black/white stripes with equal width. Figure 25 shows the comparisons of SIVV 

peaks of images with the different stripe width. Table 2 shows the stripe width and their 

SIVV peak location. In the frequency range of the texture pattern that we consider,5 the peak 

location follows a certain rule. Generally, the narrow stripe image’s SIVV peak location is 

smaller (or closer to the origin) than the wide stripe image’s peak location for the same 

texture pattern. 

The size each image in the second image set is 1024 by 1024 in Figure 26. Similarly as the 

experiment on the first image set, given the width of the stripes, the images are synthesized 

and their frequency feature are extracted. Table 3 shows the stripe width and their SIVV peak 

locations. Comparing Table 2 and Table 3, firstly, it indicates that the peak location is not 

related to the image size. It is reasonable because the x-axis of the SIVV curve is the cycles 

per pixel. The x-axis is normalized by the image size in the last step of SIVV algorithm. 

Secondly, the SIVV peak location is related with the width of the stripe directly. For 

example, the peak location of the image with four-pixel stripe width is 0.25 for both image 

with the size of 512 by 512 and the image with the size of 1024 by 1024. The peak location 

is 0.125 of the images with eight-pixel stripe width for both image sizes. For the same texture 

pattern (as our stripe pattern example), the peak location is decided by the pixel distance of 

the pattern width. The peak location follows the same rule as the first set: the SIVV peak 

location of the image with the narrow stripe is closer to the origin than the one of the image 

with wide stripe. 

On the other hand, notice that the image B in Set 2 (resolution: 1024×1024; stripe width: 8 

pixels) is just the high-resolution version of the image A in Set 1 (resolution: 512×512; stripe 

width: 4 pixels). If we resample image B in Set 2 by taking every other pixel in both column 

and row, we obtain the image A in Set 1. PPI (Pixels Per Inch) of the first image (the image 

B in Set 2) is twice as the second image (the image A in Set 1). In our case, if we scan the 

same forensic latent fingerprint with different PPI, the peak location of high PPI (B in set 2) 

 

5 We study the texture patterns in a certain frequency range. If the frequency is too high (i.e., the stripe width is 1 or 2 

pixels for the image with size 512×512, SIVV curve does not show peaks. If the frequency is too low, SIVV shows 

peaks in multiple locations (like a wave). In our study, we consider only the frequency in a certain range, which is

relatively high compared with the image size. No extreme case is considered here. 
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is half of the peak location of low PPI image (A in Set 1). Table 4 shows the comparison 

results.

In summary, the SIVV peak location is directly related to the stripe width in pixel distance. 

Table 5 shows the peak location given the stripe width. We may use it as a reference for the 

proposed peak location constraint algorithm. 

Finally, we also generate another set of synthetic image with square pattern (as shown in 

Figure 27). It shows that SIVV peak location follows the same rule; just the location of the 

peak is different given different texture pattern.
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Set 1 B-1. width: 8 (512×512)

Set 1 A-1. width: 4 (512×512)

Set 1 C-1. width: 16 (512×512)

Set 1 D-1. width: 32 (512×512)

A-2. 2D DFT power spectrum (4)

B-2. 2D DFT power spectrum (8)

C-2. 2D DFT power spectrum (16)

D-2. 2D DFT power spectrum (32)

A-3. 2D Polar spectrum (4)

B-3. 2D Polar spectrum (8)

C-3. 2D Polar spectrum (16)

D-3. 2D Polar spectrum (32)

A-4. SIVV (4)

B-4. SIVV (8)

C-4. SIVV (16)

Figure 25: SIVV on the images with the stripes pattern (image size 512×512 pixels).

D-4. SIVV (32)
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Table 2: SIVV peak locations on the images with the stripes pattern (image size 512×512 pixels)

Stripe width 4 8 16 32

Set 2 B-1. width: 8 (1024 ×1024)

Set 2 A-1. width: 4 (1024 ×1024)

Set 2 C-1. width: 16 (1024 ×1024)

Set 2 D-1. width: 32 (1024 ×1024)

A-2. 2D DFT power spectrum (4)

B-2. 2D DFT power spectrum (8)

C-2. 2D DFT power spectrum (16)

D-2. 2D DFT power spectrum (32)

A-3. 2D Polar spectrum (4)

B-3. 2D Polar spectrum (8)

C-3. 2D Polar spectrum (16)

D-3. 2D Polar spectrum (32)

A-4. SIVV (4; location 0.25)

B-4. SIVV (8; location 0.125)

C-4. SIVV (16; location 0.0625)

D-4. SIVV (32; location 0.312)

Figure 26: SIVV on the large images with the stripes pattern (image size 1024×1024 pixels). 
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Peak location 0.25 0.125 0.625 0.312

Table 3: SIVV peak locations on the large images with the stripes pattern (image size 1024×1024 pixels)

Stripe width 4 8 16 32

Peak location 0.25 0.125 0.625 0.312

Table 4: The relationship between peak locations and PPI

512×512 Strip width 4 8 16

512×512 Peak location 0.25 0.125 0.625

Double PPI of first row

(the image looks the 

same, but the resolution 

is doubled in both x, and 

y directions)

1024×1024 strip width 8 16 32

1024×1024 peak location 0.125 0.0625 0.0312

Table 5: The peak locations given the stripe pixel distance.

4 8 12 16 20 24 28 32 36 40

0.25 0.125 0.083 0.0625 0.05 0.042 0.035 0.031 0.027 0.025
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A. width: 4 (512×512) B. width: 8 (512×512) C. width: 16 (512×512) D. width: 32 (512×512)

A-4. SIVV (4; location 0.35) B-4. SIVV (8; location 0.175) C-4. SIVV (16; location:0.085) D-4. SIVV (32; Around 0.04)

Figure 27: SIVV on the images with square pattern.