NISTIR 7910

Alternative Methods of Latent 

Fingerprint Enhancement and 

Metrics for Comparing Them

Alfred S. Carasso

http://dx.doi.org/10.6028/NIST.IR.7910
NISTIR 7910

Alternative Methods of Latent 

Fingerprint Enhancement and 

Metrics for Comparing Them

Alfred S. Carasso

Applied and Computational Mathematics Division

Information Technology Laboratory

http://dx.doi.org/10.6028/NIST.IR.7910

January 2013

U.S. Department of Commerce 

Rebecca Blank, Acting Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 
ALTERNATIVE METHODS OF LATENT FINGERPRINT

ENHANCEMENT AND METRICS FOR COMPARING THEM

ALFRED S. CARASSO∗

Abstract. Digital enhancement of latent fingerprints using Photoshop processing1is the preferred methodology among law enforcement forensic experts. This report explores alternative enhancement methods that are independent of Photoshop, relying instead on such widely used scientific

image analysis packages as MATLAB, IDL, and PV-WAVE. In addition, standard image metrics are

applied to the processed images and shown capable of distinguishing among various types of enhancements. The discussion is anchored around three illustrative examples, consisting of 8 bit greyscale

latent fingerprint image pairs extracted from the NIST forensic database. It is shown that alternative

enhancement methods exist that can recover potentially significant fine scale information, such as

may elude standard forensic Photoshop processing.

Key words. latent fingerprints; digital image enhancement; image gradient norms; total variation norm.

1. Introduction. This report is a contribution to the FY 2012 research project

Metrics for Manipulation and Enhancement of Forensic Images, sponsored by the

NIST Information Technology Laboratory. That project is primarily focused on the

processes used for digital image enhancement of poor quality latent fingerprints left

unintentionally at a crime scene. To facilitate this study, law enforcement forensic

experts provided NIST with a database of fingerprint image pairs, before and after

enhancement. Such enhancements result exclusively from Adobe Photoshop image

processing, which is the methodology preferred by law enforcement professionals.

The discussion in this report is anchored around three illustrative examples, labeled (A), (B), and (C), and consisting of 8 bit greyscale latent fingerprint image pairs,

extracted from the Originals/White Powder Developed Prints/NIST Study series in

the above database.

In recent years, skepticism has occasionally been expressed about the reliability

of digital enhancements of latent fingerprints, [1], [2]. The Photoshop methodology

does not provide a digital record of the various steps applied in enhancing the image. Moreover, the particular combination of Photoshop steps used in the process

may inadvertently eliminate vital fine scale information, information that might exclude a suspect. This report discusses alternative approaches to latent fingerprint

enhancement that are independent of Photoshop. Consideration of such alternative

enhancements, alongside the traditional Photoshop result, may better inform the subsequent, crucial, matching and identification procedure. An important characteristic

of the proposed methods is that they yield reproducible results, as they are based on

the use of simple commands from such widely used scientific image analysis software

packages as MATLAB [3], IDL [4], and PV-WAVE [5]. The uses and purposes of

these commands are documented and illustrated in the accompanying user’s manuals.

In addition, [6] provides a thorough discussion of the theoretical ideas underlying the

various enhancement techniques available in the above packages. Note that implementations vary among different packages, and the same enhancement technique can

∗Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, MD 20899. (alfred.carasso@nist.gov).

1Mention of commmercial products or services in this report does not imply NIST appproval

or endorsement of these products or services, nor does it imply that such products or services are

necessarily the best available for the intended purpose.

1
2 ALFRED S. CARASSO

produce slightly different images in different packages. Such enhancement methods

are designed to bring into better visual range potentially significant structures that

are present in the original image, but not easily discernible.

This report includes software routines that can be used with MATLAB and IDL

packages to enhance latent fingerprints. Here, these routines are applied to the three

original latent print images (A), (B), and (C). It should be noted that image (C)

is substantially different from image (A), although both images look similar at first

glance. Each of these images is subjected to five distinct alternative enhancements,

in addition to the Photoshop enhancement included in the NIST database. Standard

image norms are then calculated for each processed image. Such norms confirm and

quantify the noticeable differences that are evident in some of these enhancements.

1.1. Default parameter values and further possible enhancements. In

the examples discussed below using the software routines in Section 3, only the simplest forms of image analysis commands are used. In fact, each command can be

applied with a variety of options by specifying desired values in an associated parameter list. Here, such parameter values are omitted, resulting in default values being

assumed. Using the routines of Section 3 with more sophisticated parameter choices,

can produce a much larger variety of distinct enhancements than are shown below for

images (A), (B) and (C).

2. Image metrics. The latent fingerprint images discussed in this report are 8

bit grey scale tiff images, with intensity values ranging from 0 to 255. These images

are large, rectangular, and of diverse sizes, with the y dimension exceeding the x

dimension. The software routines described in Section 3 can accept and process these

diverse size images. However, post processing, all images in this report are reformatted

as follows in order to simplify image manipulations, and enable viewing of multiple

post-processed images on a single screen. The y size of each image is reduced to 1024

pixels; the x size is then reduced by the appropriate amount so as to maintain the

original x/y ratio. Next, the x size of the image is increased to 1024 pixels by zero

padding in the x direction. All image norms defined below and used in Tables 1 − 6,

refer to these zero padded 1024 × 1024 pixel images, denoted by f(x, y).

The following discrete L

1 and L2

image norms, as well as the discrete L

1 and

L

2

image gradient norms, will be found helpful in distinguishing among the various

enhancement approaches discussed below. The image gradient norms are particularly

sensitive to the presence of fine scale information in the processsed image.

k f kp=

(

(1024)−2

1024

X

x,y=1

|f(x, y)|

p

)1/p

(1) , p = 1, 2,

and

k ∇f kp=

(

(1024)−2

1023

X

x,y=1

({f

x

(x, y)}

2 + {fy

(x, y)}

2

)

p/2

)1/p

(2) , p = 1, 2,

where

f

x

(x, y) = (1024) (f(x + 1, y) − f(x, y)),

f

y

(3) (x, y) = (1024) (f(x, y + 1) − f(x, y)).
ALTERNATIVE ENHANCEMENTS OF LATENT FINGERPRINTS 3

The norm k ∇f k1 in Eq.(2), sometimes called the discrete ‘Total Variation’ or TV

norm, plays an important role in image analysis. Unexpectedly, the L

2

image gradient

norm, k ∇f k2, turns out to be more significant in the examples below.

3. Alternative enhancements using simple software routines. The enhancement methods used in this report are itemized below. The sketchy descriptions

in this itemized list can be supplemented by consulting [3], [4], and [5]. In particular,

the various options available with each method are discussed in detail in these user’s

guides. Instructive analytical discussions concerning these and other enhancement

techniques may be found in [6].

• Contrast Adjustment. (MATLAB imadjust; IDL bytscl). This method enhances the image by changing underlying image intensities through stretching

and brightening the original intensity range.

• Sobel Filtering. (IDL sobel). This is an edge enhancement technique based

on differentiating the image, using a special difference approximation to the

gradient at each point.

• Unsharp Masking. (IDL unsharp mask). This is a useful method for highpass filtering, that highlights edges and small sharp features in the displayed

image. It consists of subtracting a Gaussian smoothed version from the original image, so as to better emphasize the high frequency components in the

original.

• Histogram Equalization. (MATLAB histeq; IDL hist equal). In many images of interest, pixel values tend to reside in a few narrow subintervals of

the full 8 bit interval [0, 255]. One can maximize the information content in

the displayed image by spreading the distribution of pixel values, so as to

more evenly cover the full range from 0 to 255. This helps bring small scale

details into better visual range, while typically changing the appearance of

the image.

• Adaptive Histogram Equalization. (IDL adapt hist equal). This is a more

refined form of histogram equalization, whereby contrast enhancement is

adapted to the local region surrounding each pixel. This method is believed

to produce superior images.

3.1. MATLAB routine. The following MATLAB m-file is used for contrast

adjustment and histogram equalization. Additional commands can be added to this

m-file, and more sophisticated versions of each command can be created by specifying

appropriate parameters.

% m-file MATLABfinger.m

%SIMPLE MATLAB CODE FOR IMAGE ENHANCEMENT

%APPLY BY TYPING ‘MATLABfinger’ in MATLAB

%ASSUMES INPUT 8bit GREY SCALE TIFF IMAGE ’latentprint.tiff’

%RETURNS ENHANCED TIFF IMAGE IN FILE ’matsharp.tiff’

%RETURNS REVERSED ENHANCED TIFF IMAGE IN FILE ’revmatsharp.tiff’

% ACTIVATE DESIRED COMMANDS BY DELETING ‘ % ’ SYMBOLS BELOW
4 ALFRED S. CARASSO

image=imread(’latentprint.tiff’);

%image=imadjust(image);

image=histeq(image);

reverseimage=(255-image);

imshow(image);

%imshow(reverseimage);

imwrite(image, ’matsharp.tiff’);

imwrite(reverseimage, ’revmatsharp.tiff’);

3.2. IDL routine. This routine is used for Sobel filtering, unsharp masking,

and adaptive histogram equalization. As above, additional commands can be added,

and more sophisticated versions of each of these commands can be created.

;pro-file IDLfinger.pro

;SIMPLE IDL CODE FOR ENHANCING IMAGES

;APPLY BY TYPING ‘.run IDLfinger’ in IDL

;ASKS USER TO PROVIDE INPUT 8bit GREYSCALE TIFF IMAGE

;ASKS USER TO SPECIFY X size and Y size of INPUT TIFF IMAGE

;RETURNS ENHANCED IMAGE IN FILE "sharp.tiff"

;RETURNS REVERSED ENHANCED IMAGE IN FILE "revsharp.tiff"

file1=’ ’

read,’enter filename: ’,file1

openu,1,file1

x1=0

y1=0

read,"enter xsize of image: ",x1

read,"enter ysize of image: ",y1

window,0,xsize=x1,ysize=y1

a = assoc(1,bytarr(x1,y1,/nozero))

image = a(0)

close, 1

;ACTIVATE DESIRED COMMANDS BY DELETING ‘ ; ’ SYMBOLS BELOW

;image=bytscl(image)

;image=hist equal(image)

image=adapt hist equal(image)

;image=unsharp mask(image)

;image=roberts(image)

;image=sobel(image)

;image=5.0*image

reverseimage=255-image

tvscl, image

;tvscl, reverseimage

write tiff, ’idlsharp.tiff’, image

write tiff, ’idlrevsharp.tiff’, reverseimage

end

4. Enhancing images (A), (B), and (C). Figure 1 shows the results of applying the MATLAB commands for contrast adjustment and histogram equalization

to the original latent print (A), and compares them to the forensic Photoshop process.
ALTERNATIVE ENHANCEMENTS OF LATENT FINGERPRINTS 5

The corresponding reverse images are shown in Figure 2 on the adjoining page. The

MATLAB contrast adjusted image in Figure 1 differs somewhat from the Photoshop

image, a difference that is picked up in the L

2

image and image gradient norms in

Table 1, although not in the corresponding L

1 norms. Also, the reverse MATLAB

contrast image is sharper than the reverse Photoshop image. However, the MATLAB

histogram equalized image in Figure 1, and its reverse counterpart in Figure 2, are

vastly different enhancements. This is reflected in the substantially larger L

1 and

L

2

image and image gradient norms in Table 1. Evidently, considerable fine scale

information has been dredged up by this particular technique. It may be unwise to

dismiss that information as noise. In the lower right images in Figures 1 and 2, there

are vertical and horizontal lines that criss-cross the fingerprint, as well as various other

features that are not easily discernible in the forensic Photoshop image. Some of these

details may be significant. At the very least, the histogram equalized image alerts the

forensic analyst to the possibly important fine structure present in the original image,

information that is not recovered by the Photoshop process.

Figure 3 shows additional enhancements applied to the original print (A), using

IDL Sobel filtering, IDL unsharp making, and IDL adaptive histogram equalization.

The corresponding reverse images are shown in Figure 4 on the adjoining page. Here,

and throughout this report, intensities were artificially increased after Sobel filtering

and unsharp masking, to compensate for the faint imagery that generally results from

applying these two techniques. The image gradient norms in Table 2 for the IDL Sobel

and IDL unsharp masking images, are in general agreement with those for the Photoshop and MATLAB contrast adjusted images in Table 1. In fact, these four enhanced

images and their reversed counterparts, are roughly of the same general quality, although important differences exist and can be detected by the trained eye. On the

other hand, the IDL adaptive histogram equalized image in Figure 3 is distinctly different from the other five enhancements in image (A). Despite the unwelcome amount

of background detail in the IDL adaptive case, a higher quality reconstruction of the

actual fingerprint has been realized in Figure 3, and in the counterpart reverse image

in Figure 4. This is accompanied by larger L

1 and L2

image and image gradient

norms, than was the case in the MATLAB non adaptive case in Figure 1.

Clearly, a more complete picture emerges of the fingerprint in latent print (A),

when the forensic Photoshop processed image in Figure 1 is complemented by the

alternative independent enhancements displayed in Figures 1 through 4.

Figures 5 through 8 deal with latent print (B). Here, the accompanying forensic

Photoshop processed image is of poor quality, as shown in Figure 5. Significant

improvement is provided by the MATLAB enhancements in Figures 5 and 6, with the

histogram equalized image providing the most information. Again, note the presence

of horizontal and verical streaks that criss-cross the lower right images in Figures 5

and 6, as well as some other features not evident in the other images in Figures 5

and 6. The IDL Sobel and unsharp masking images in Figures 7 and 8 appear less

sharp than the MATLAB contrast adjusted image in Figures 5 and 6. These two IDL

images have approximately the same value for k ∇f k1 as has the MATLAB contrast

adjusted image, but the latter image has a significantly larger value for k ∇f k2. A

similar divergence between L

1 and L2 norms was already observed in the discussion

of latent print (A). The IDL adaptive histogram equalized images in Figures 7 and

8 may not appear helpful when viewed on the printed page. However, when viewed

on a high resolution device, such as an active matrix, backlit, LCD monitor, these

IDL adaptive images are seen to contain higher quality information than is available
6 ALFRED S. CARASSO

in the MATLAB non adaptive case in Figures 5 and 6. In summary, in this difficult

latent print, substantially more useful information can be obtained using the above

alternative enhancements, than was provided in the forensic Photoshop processed

image.

Behavior in latent print (C), illustrated in Figures 9 through 12, is quite similar

to that in latent print (A). The MATLAB contrast adjusted image is again sharper

than the forensic Photoshop image. This is picked up in the k ∇f k2 norm in Table

5, but not in the k ∇f k1 norm. The MATLAB histogram equalized image recovers

potentially significant information that is not visible in the Photoshop enhancement.

This is confirmed by the substantially larger image gradient norms. The IDL Sobel

and IDL unsharp masking images are of roughly the same quality as the forensic

Photoshop image, with approximately the same image gradient norms. Finally, in

Figures 11 and 12, despite the distracting amount of background detail, the IDL

adaptive equalized images, viewed on a modern LCD monitor, provide more useful

information in the lower half of latent print (C) than do the non adaptive MATLAB

images in Figures 9 and 10.

5. Concluding remarks and remaining questions. For the class of latent

fingerprints represented by the above three exmples, Sobel filtering and unsharp masking tend to produce enhancements of the same general quality as the forensic Photoshop process. However, contrast adjustment using MATLAB imadjust, or IDL bytscl,

typically produces higher quality images, accompanied by larger values for k ∇f k2.

Histogram equalization using MATLAB histeq, or IDL hist equal, produces a

different category of enhancement, with significantly larger values for k ∇f k2, along

with recovery of potentially significant fine scale information typically not present in

the forensic Photoshop processed image. Adaptive histogram equalization, using IDL

adapt hist equal, tends to bring up considerable background detail, while producing

higher quality fingerprint reconstructions than may be feasible with non-adaptive

equalization. In the case of latent print (A) for example, the dark patches obscuring

the fingerprint image in the lower right corner of Figure 2, are not present in the

image in the lower right corner of Figure 4.

As previously noted, the alternative enhancements displayed in Figures 1 through

12 resulted from the use of MATLAB and/or IDL commands in their simplest forms,

where default values were assumed for the associated optional parameters. It would

be instructive to explore the same enhancement methods with more sophisticated

parameter choices.

A question of major interest is whether the additional information provided in

some of these alternative enhancements leads to the same matches obtained using the

forensic Photoshop process. If that is not the case, how much smoothing of the new

enhancement is necessary to recover the Photoshop induced matches ?

An instructive line of inquiry would start with a histogram equalized image displaying substantial fine scale detail. Subjecting that image to systematic ‘slow motion’ fractional diffusion denoising [7], would create a sequence of gradually

smoother enhancements. How would the resulting matches vary with the different

images along that sequence ?

REFERENCES

[1] F. McRoberts and S. Mills, Digitized prints can point finger at the innocent, Chicago Tribune, January 3 2005.
ALTERNATIVE ENHANCEMENTS OF LATENT FINGERPRINTS 7

[2] F. McRoberts and S. Mills, US seeks review of fingerprint techniques, Chicago Tribune,

February 21 2005.

[3] Image Processing Toolbox User’s Guide (2012), The MathWorks, Inc. Natick, MA 01760.

[4] ITTVIS Interactive Data Language (IDL), http://www.exelisvis.com/IDL.aspx

[5] PV-Wave 7.5 Image Processing Toolkit User’s Guide, Visual Numerics, Inc. Houston, TX

77042.

[6] W. K. Pratt, Digital Image Processing, (2012), Wiley-Interscience, New York.

[7] A. S. Carasso and A. E. Vladar ́ , Fractional diffusion, low exponent L ́evy stable laws,

and ‘slow motion’ denoising of Helium Ion microscope nanoscale imagery, Journal of

Research of the National Institute of Standards and Technology, Volume 117 (2012),

http://dx.doi.org/10.6028/jres.117.006
8 ALFRED S. CARASSO

 

ORIGINAL LATENT FINGERPRINT FORENSIC PHOTOSHOP PROCESS

MATLAB CONTRAST ADJUSTMENT MATLAB HISTOGRAM EQUALIZATION

ALTERNATIVE ENHANCEMENTS OF LATENT PRINT (A)

Fig. 1. Enhancements of latent fingerprint (A). Original print, accompanying forensic Photoshop enhancement, and two alternative MATLAB enhancements. Significant enhancement of background features is apparent in histogram equalized image. This is reflected in the image gradient

norms in Table 1.

TABLE 1.

Image metrics in enhancements of latent print (A).

Image f(x, y) k f k1 k f k2 k ∇f k1 k ∇f k2

Original Latent Print (A) 16 18 2600 3900

Forensic Photoshop (A) 61 75 25000 36000

Matlab Contrast Adj. (A) 60 80 25000 40000

Matlab Histogram Eq. (A) 112 138 42000 60000
ALTERNATIVE ENHANCEMENTS OF LATENT FINGERPRINTS 9

 

ORIGINAL LATENT FINGERPRINT FORENSIC PHOTOSHOP PROCESS

MATLAB CONTRAST ADJUSTMENT MATLAB HISTOGRAM EQUALIZATION

REVERSE IMAGES IN ENHANCED LATENT PRINT (A)

Fig. 2. Reverse images in the latent print (A) enhancements shown in Figure 1.
10 ALFRED S. CARASSO

 

ORIGINAL LATENT FINGERPRINT IDL SOBEL FILTERING ( x 2)

 IDL UNSHARP MASKING (x 5) IDL ADAPTIVE HISTOGRAM EQZN.

ADDITIONAL ENHANCEMENTS OF LATENT PRINT (A)

Fig. 3. Additional enhancements of latent fingerprint (A). Original print and three alternative

IDL enhancements. Intensities were doubled after Sobel filtering to improve visibility. Similarly,

intensities were quintupled after unsharp masking. Noticeably different adaptive histogram equalized

image displays considerable background information. This is reflected in the larger image gradient

norms in Table 2.

TABLE 2.

Image metrics in additional enhancements of latent print (A).

Image f(x, y) k f k1 k f k2 k ∇f k1 k ∇f k2

Original Latent Print (A) 16 18 2600 3900

IDL Sobel Filtering (×2) (A) 31 45 24000 35000

IDL Unsharp Mask (×5) (A) 86 97 24000 37000

IDL Adaptive Hist. Eq. (A) 127 150 54000 70000
ALTERNATIVE ENHANCEMENTS OF LATENT FINGERPRINTS 11

 

ORIGINAL LATENT FINGERPRINT IDL SOBEL FILTERING (x 2)

 IDL UNSHARP MASKING (x 5) IDL ADAPTIVE HISTOGRAM EQZN.

 REVERSE IMAGES IN ENHANCED LATENT PRINT (A)

Fig. 4. Reverse images in the additional latent print (A) enhancements shown in Figure 3.
12 ALFRED S. CARASSO

 

ORIGINAL LATENT FINGERPRINT FORENSIC PHOTOSHOP PROCESS

MATLAB CONTRAST ADJUSTMENT MATLAB HISTOGRAM EQUALIZATION

ALTERNATIVE ENHANCEMENTS OF LATENT PRINT (B)

Fig. 5. Enhancements of latent fingerprint (B). Original print, accompanying forensic Photoshop enhancement, and two alternative MATLAB enhancements. As in Figure 1, histogram equalized image shows substantial small scale background features, and has larger image gradient norms.

TABLE 3.

Image metrics in enhancements of latent print (B).

Image f(x, y) k f k1 k f k2 k ∇f k1 k ∇f k2

Original Latent Print (B) 15 16 2500 4200

Forensic Photoshop (B) 29 33 8000 12000

Matlab Contrast Adj. (B) 52 69 26000 43000

Matlab Histogram Eq. (B) 115 140 49000 69000
ALTERNATIVE ENHANCEMENTS OF LATENT FINGERPRINTS 13

 

ORIGINAL LATENT FINGERPRINT FORENSIC PHOTOSHOP PROCESS

MATLAB CONTRAST ADJUSTMENT MATLAB HISTOGRAM EQUALIZATION

REVERSE IMAGES IN ENHANCED LATENT PRINT (B)

Fig. 6. Reverse images in the latent print (B) enhancements shown in Figure 5

.
14 ALFRED S. CARASSO

 

ORIGINAL LATENT FINGERPRINT IDL SOBEL FILTERING (x 2)

 IDL UNSHARP MASKING (x 5) IDL ADAPTIVE HISTOGRAM EQZN.

ADDITIONAL ENHANCEMENTS OF LATENT PRINT (B)

Fig. 7. Additional enhancements of latent fingerprint (B). Original print and three alternative

IDL enhancements. As in Figure 3, intensities were doubled after Sobel filtering, and quintupled

after unsharp masking. Distinctly different adaptive histogram equalized image displays considerable

small scale detail, as is confirmed by the larger image gradient norms in Table 4.

TABLE 4.

Image metrics in additional enhancements of latent print (B).

Image f(x, y) k f k1 k f k2 k ∇f k1 k ∇f k2

Original Latent Print (B) 15 16 2500 4200

IDL Sobel Filtering (×2) (B) 27 43 24000 38000

IDL Unsharp Mask (×5) (B) 79 87 23000 37000

IDL Adaptive Hist. Eq. (B) 135 156 62000 79000
ALTERNATIVE ENHANCEMENTS OF LATENT FINGERPRINTS 15

 

ORIGINAL LATENT FINGERPRINT IDL SOBEL FILTERING (x 2)

 IDL UNSHARP MASKING (x 5) IDL ADAPTIVE HISTOGRAM EQZN.

 REVERSE IMAGES IN ENHANCED LATENT PRINT (B)

Fig. 8. Reverse images in the additional latent print (B) enhancements shown in Figure 7.
16 ALFRED S. CARASSO

 

ORIGINAL LATENT FINGERPRINT FORENSIC PHOTOSHOP PROCESS

MATLAB CONTRAST ADJUSTMENT MATLAB HISTOGRAM EQUALIZATION

ALTERNATIVE ENHANCEMENTS OF LATENT PRINT (C)

Fig. 9. Enhancements of latent fingerprint (C). Original print, accompanying forensic Photoshop enhancement, and two alternative MATLAB enhancements. As in Figure 1, histogram equalized image shows substantial small scale background features, and has larger image gradient norms.

TABLE 5.

Image metrics in enhancements of latent print (C).

Image f(x, y) k f k1 k f k2 k ∇f k1 k ∇f k2

Original Latent Print (C) 17 18 2800 4200

Forensic Photoshop (C) 62 76 29000 37000

Matlab Contrast Adj. (C) 58 78 29000 44000

Matlab Histogram Eq. (C) 113 139 47000 65000
ALTERNATIVE ENHANCEMENTS OF LATENT FINGERPRINTS 17

 

ORIGINAL LATENT FINGERPRINT FORENSIC PHOTOSHOP PROCESS

MATLAB CONTRAST ADJUSTMENT MATLAB HISTOGRAM EQUALIZATION

REVERSE IMAGES IN ENHANCED LATENT PRINT (C)

Fig. 10. Reverse images in the latent print (C) enhancements shown in Figure 9.
18 ALFRED S. CARASSO

 

ORIGINAL LATENT FINGERPRINT IDL SOBEL FILTERING (x 2)

 IDL UNSHARP MASKING (x 5) IDL ADAPTIVE HISTOGRAM EQZN.

ADDITIONAL ENHANCEMENTS OF LATENT PRINT (C)

Fig. 11. Additional enhancements of latent fingerprint (C). Original print and three alternative

IDL enhancements. As in Figure 3, intensities were doubled after Sobel filtering, and quintupled

after unsharp masking. Distinctly different adaptive histogram equalized image displays considerable

small scale detail, as is confirmed by the larger image gradient norms in Table 6.

TABLE 6.

Image metrics in additional enhancements of latent print (C).

Image f(x, y) k f k1 k f k2 k ∇f k1 k ∇f k2

Original Latent Print (C) 17 18 2800 4200

IDL Sobel Filtering (×2) (C) 31 45 25000 36000

IDL Unsharp Mask (×5) (C) 88 98 26000 39000

IDL Adaptive Hist. Eq. (C) 129 151 59000 77000
ALTERNATIVE ENHANCEMENTS OF LATENT FINGERPRINTS 19

 

ORIGINAL LATENT FINGERPRINT IDL SOBEL FILTERING (x 2)

 IDL UNSHARP MASKING (x 5) IDL ADAPTIVE HISTOGRAM EQZN.

 REVERSE IMAGES IN ENHANCED LATENT PRINT (C)

Fig. 12. Reverse images in the additional latent print (C) enhancements shown in Figure 11.