Development of NFIQ 2.0 

Evaluation of Potential Image Features for Quality 

Assessment 

Version 0.5 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

Table of Contents 

1 Introduction................................................................................................................................... 5

2 Features and Feature Extraction Methods................................................................................. 7

2.1 NFIQ1 Features ..................................................................................................................... 7

2.2 FingerJet FX Features ........................................................................................................... 7

2.2.1 Number of Minutiae ........................................................................................................... 7

2.2.2 Fingerprint Quality ............................................................................................................. 7

2.2.3 Average Minutiae Quality................................................................................................... 8

2.2.4 Minutiae Quality 0 .............................................................................................................. 8

2.2.5 Minutiae Quality 1 .............................................................................................................. 8

2.2.6 Minutiae Quality 2 .............................................................................................................. 8

2.2.7 Minutiae Quality 3 .............................................................................................................. 8

2.2.8 Minutiae Quality 4 .............................................................................................................. 9

2.2.9 Minutiae Quality 5 .............................................................................................................. 9

2.2.10 Minutiae Quality 6.......................................................................................................... 9

2.2.11 Minutiae Quality 7.......................................................................................................... 9

2.2.12 Minutiae Quality 8.......................................................................................................... 9

2.2.13 Minutiae Quality 9........................................................................................................ 10

2.2.14 Minutiae Quality 10...................................................................................................... 10

2.3 hda Features ........................................................................................................................ 10

2.3.1 Notation............................................................................................................................ 10

2.3.2 Frequency Domain Analysis............................................................................................ 10

2.3.3 Gabor ............................................................................................................................... 12

2.3.4 Gabor Segment................................................................................................................ 15

2.3.5 Gabor Shen...................................................................................................................... 16

2.3.6 Local Clarity Score........................................................................................................... 18

2.3.7 Mu .................................................................................................................................... 21

2.3.8 Mu Mu Block .................................................................................................................... 22

2.3.9 Mu Mu Sigma Block......................................................................................................... 22

2.3.10 Mu Sigma Block........................................................................................................... 22

2.3.11 Orientation Certainty Level.......................................................................................... 22

2.3.12 Orientation Flow .......................................................................................................... 24

2.3.13 Radial Power Spectrum............................................................................................... 26

2.3.14 Ridge Valley Uniformity............................................................................................... 27

2.3.15 Sigma .......................................................................................................................... 29

2.3.16 Sigma Mu Block........................................................................................................... 29

3 Evaluation Approach.................................................................................................................. 30

3.1 Methodology......................................................................................................................... 30

3.1.1 Spearman Correlation and Scatter Plots ......................................................................... 30

3.1.2 Evaluation by ROC Curves.............................................................................................. 30

3.1.3 ERC Evaluation................................................................................................................ 31

3.2 Data basis ............................................................................................................................ 32

3.3 Limitations ............................................................................................................................ 32

4 Evaluation Results...................................................................................................................... 34

4.1 Evaluation of the NFIQ1-Features ....................................................................................... 34

4.1.1.1 Correlation and Scatter Plots .................................................................................. 34

4.1.1.2 ROC curve evaluation............................................................................................. 35

4.1.1.3 ERC Evaluation....................................................................................................... 37

4.2 Evaluation of the FingerJetFX Features .............................................................................. 38

4.2.1.1 ROC Curve Evaluation............................................................................................ 40

4.2.1.2 ERC Evaluation....................................................................................................... 42

4.3 Evaluation of the Features Implemented by hda ................................................................. 43

2 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

3 

4.3.1.1 Correlation and scatter plots ................................................................................... 45

4.3.1.2 ROC Curve Evaluation............................................................................................ 47

4.3.1.3 ERC Evaluation....................................................................................................... 52

4.4 Overall Comparison ............................................................................................................. 58

4.4.1 Correlation ....................................................................................................................... 58

4.4.2 ROC Curve Evaluation .................................................................................................... 59

4.4.3 ERC evaluation................................................................................................................ 62

4.4.4 Summary.......................................................................................................................... 63

5 References................................................................................................................................... 65
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

List of Figures 

Figure 1: Relationship between quality and system performance (taken from [1]) _______________________ 5

Figure 2: ROC curves of NFIQ1 features for provider 28 _________________________________________ 36

Figure 3: ROC curves of NFIQ1 features for provider 63 _________________________________________ 36

Figure 4: ROC curves of NFIQ1 features for provider 83 _________________________________________ 37

Figure 5: ERC of NFIQ 1 features at 3% FNMR for provider 28 ___________________________________ 38

Figure 6: ERC of NFIQ 1 features at 10% FNMR for provider 28 __________________________________ 38

Figure 7: ROC curves of FingerJet FX features for provider 28 ____________________________________ 40

Figure 8: ROC curves of FingerJet FX features for provider 63 ____________________________________ 41

Figure 9: ROC curves of FingerJet FX features for provider 83 ____________________________________ 41

Figure 10: ERC of FingerJetFX features at 3% FNMR for provider 28 ______________________________ 42

Figure 11: ERC of FingerJetFX features at 10% FNMR for provider 28 _____________________________ 42

Figure 12: Provider 28 ROC curves for hda features_____________________________________________ 48

Figure 13: Provider 63 ROC curves for hda features_____________________________________________ 49

Figure 14: ROC curves for hda features for Provider 63 (top line) and provider 83_____________________ 49

Figure 15: Provider 28 ROC curves of best had features__________________________________________ 50

Figure 16: Provider 63 ROC curves of best features _____________________________________________ 50

Figure 17: Provider 83 ROC curves of best features _____________________________________________ 51

Figure 18: ERC for hda features at 3% FNMR (top line) and 10% FNMR (bottom line) for provider 28_____ 53

Figure 19: Density function of genuine scores for provider 28 _____________________________________ 54

Figure 20: Scatter plot of genuine scores and Mu Mu Sigma Block__________________________________ 55

Figure 21: Scatter plot of genuine scores and Mu Sigma Block_____________________________________ 55

Figure 22: Scatter plot of genuine scores and Mu _______________________________________________ 56

Figure 23: Scatter plot of genuine scores and Sigma Sigma Block __________________________________ 56

Figure 24: Scatter plot of Mu Sigma Block and Mu Mu Sigma Block ________________________________ 57

Figure 25: Scatter plot of Mu Sigma Block and Mu ______________________________________________ 57

Figure 26: Scatter plot of Mu Sigma Block and Sigma Sigma Block _________________________________ 57

Figure 27: Provider 28 ROC curves of top performing features ____________________________________ 60

Figure 28: Provider 63 ROC curves of top performing features ____________________________________ 60

Figure 29: Provider 83 ROC curves of top performing features ____________________________________ 61

Figure 30: Provider 28 ROC curves of next best performing features ________________________________ 61

Figure 31: Provider 63 ROC curves of next best performing features ________________________________ 62

Figure 32: Provider 83 ROC curves of next best performing features ________________________________ 62

Figure 33: Best features in ERC at 3% FNMR for provider 28 _____________________________________ 63

Figure 34: Best features in ERC at 10% FNMR for provider 28 ____________________________________ 63

List of Tables 

Table 1: Description of NFIQ1 Features................................................................................................................ 7

Table 2: Spearman correlation coefficients for NFIQ1 features .......................................................................... 34

Table 3: Optimal selection criteria for ROC curves ............................................................................................. 35

Table 4: Spearman correlation coefficients for NFIQ1 features .......................................................................... 39

Table 5: Optimal selecton criteria for ROC curves .............................................................................................. 40

Table 6: Configurations used for feature computation ......................................................................................... 44

Table 7: Equivalent, almost identical or constant values of the feature configurations....................................... 44

Table 8: Non-redundant configurations giving relatively good performance for the feature............................... 45

Table 9: Spearman correlation coefficients for hda features................................................................................ 46

Table 10: Optimal selection criteria for hda features.......................................................................................... 47

Table 11: Top ranks of ROC evaluation for hda features..................................................................................... 52

Table 12: Top ranks of ERC evaluation for hda features ..................................................................................... 58

Table 13: Spearman correlation coefficients of best performing features............................................................ 59

Table 14: Top ranks of ERC evaluation for all features ....................................................................................... 64

4 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

5 

1 Introduction 

According to ISO /IEC 29794-1 [1], the quality score output by a biometric quality 

assessment algorithm should convey a predicted utility of the biometric sample, 

where the utility of the biometric sample reflects its impact on the recognition 

performance of the biometric system. This relationship between quality and system 

performance is depicted in Figure 1. 

Figure 1: Relationship between quality and system performance (taken from [1]) 

In accordance with these requirements, the approach taken for the development of 

the NFIQ and the NFIQ2.0 algorithm is to train a machine learning algorithm (e.g. a 

neural network) to predict the utility of fingerprints from global and local structures of 

the fingerprint image, henceforth referred to as quality features. The potential to 

predict the utility heavily depends on the significance of the quality features for the 

image properties influencing its utility, i.e. the more indicative the quality features are 

for the expected biometric performance of the fingerprint, the better can an algorithm 

predict the utility. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

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Several quality features have been proposed in the literature, in particular in ISO/IEC 

29794-4 [6], [12] and the NIST report on the development of the NFIQ algorithm [12], 

but also in other publications, e.g. [1], [2], [3], [4], [7], [10], [11] and [13]. 

In this document, we report on the implementation and systematic evaluation of a 

large number of features, including all features proposed in [6], [12] and [12], with 

respect to their eligibility for predicting fingerprint utility. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

7 

2 Features and Feature Extraction Methods 

2.1 NFIQ1 Features 

The NFIQ1 features are implemented by the NBIS package and have been used in 

the training of the NFIQ algorithm [12]. They are based on the minutiae output by 

MINDTCT and a quality map [13]. The quality map is computed on segmentation of 

the image into 8x8 pixel blocks. This quality map and the local image contrast are 

used by MINDTCT to compute a quality value for each minutia. 

Feature Description 

foreground number of blocks that are quality 1 or better 

total #of minutia number of total minutiae found in the fingerprint (using 

min05 number of minutiae that have quality 0.5 or better 

min06 number of minutiae that have quality 0.6 or better 

min075 number of minutiae that have quality 0.75 or better 

min08 number of minutiae that have quality 0.8 or better 

min09 number of minutiae that have quality 0.9 or better 

quality zone 1 percentage of the foreground blocks of quality map with quality =1 

quality zone 2 percentage of the foreground blocks of quality map with quality =2 

quality zone 3 percentage of the foreground blocks of quality map with quality =3 

quality zone 4 percentage of the foreground blocks of quality map with quality =4 

Table 1: Description of NFIQ1 Features. 

2.2 FingerJet FX Features 

2.2.1 Number of Minutiae 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeCount 

identifier 

Also known as MinutiaeCount 

Description 

This value expresses the number of minutia extracted by the open source edition of 

DigitalPersona’s FingerJetFX algorithm. 

2.2.2 Fingerprint Quality 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_FingerprintQuality 

identifier 

Also known as FingerprintQuality 

Description 

This value expresses the quality of the fingerprint image returned by the open source 

edition of DigitalPersona’s FingerJetFX algorithm. 

Notes 

The FingerJetFX algorithm constantly returns the value 86. Hence, this feature can 

be considered irrelevant. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

8 

2.2.3 Average Minutiae Quality

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_AverageMinutiaeQuality 

identifier 

Also known as AverageMinutiaeQuality 

Description 

This value expresses the average (arithmetic mean) quality of all returned minutiae 

by the open source edition of DigitalPersona’s FingerJetFX algorithm. 

2.2.4 Minutiae Quality 0 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_0 

identifier 

Also known as MinutiaeQuality 0 

Description 

This value expresses the percentage of all detected minutiae quality values that have 

a value of 0 are greater than 100. (Absolute number of minutiae quality values that 

are 0 or >100 divided by the absolute number of detected minutiae). 

2.2.5 Minutiae Quality 1 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_1 

identifier 

Also known as MinutiaeQuality 1 

Description 

This value expresses the percentage of all detected minutiae quality values that are 

in the range from 1 until 10 (Absolute number of minutiae quality values that are >= 1 

and <= 10 divided by the absolute number of detected minutiae). 

2.2.6 Minutiae Quality 2 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_2 

identifier 

Also known as MinutiaeQuality 2 

Description 

This value expresses the percentage of all detected minutiae quality values that are 

in the range from 11 until 20 (Absolute number of minutiae quality values that are >= 

11 and <= 20 divided by the absolute number of detected minutiae). 

2.2.7 Minutiae Quality 3 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_3 

identifier 

Also known as MinutiaeQuality 3 

Description 

This value expresses the percentage of all detected minutiae quality values that are 

in the range from 21 until 30 (Absolute number of minutiae quality values that are >= 

21 and <= 30 divided by the absolute number of detected minutiae). 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

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2.2.8 Minutiae Quality 4 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_4 

identifier 

Also known as MinutiaeQuality 4 

Description 

This value expresses the percentage of all detected minutiae quality values that are 

in the range from 31 until 40 (Absolute number of minutiae quality values that are >= 

31 and <= 40 divided by the absolute number of detected minutiae). 

2.2.9 Minutiae Quality 5

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_5 

identifier 

Also known as MinutiaeQuality 5 

Description 

This value expresses the percentage of all detected minutiae quality values that are 

in the range from 41 until 50 (Absolute number of minutiae quality values that are >= 

41 and <= 50 divided by the absolute number of detected minutiae). 

2.2.10 Minutiae Quality 6 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_6 

identifier 

Also known as MinutiaeQuality 6 

Description 

This value expresses the percentage of all detected minutiae quality values that are 

in the range from 51 until 60 (Absolute number of minutiae quality values that are >= 

51 and <= 60 divided by the absolute number of detected minutiae). 

2.2.11 Minutiae Quality 7 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_7 

identifier 

Also known as MinutiaeQuality 7 

Description 

This value expresses the percentage of all detected minutiae quality values that are 

in the range from 61 until 70 (Absolute number of minutiae quality values that are >= 

61 and <= 70 divided by the absolute number of detected minutiae). 

2.2.12 Minutiae Quality 8

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_8 

identifier 

Also known as MinutiaeQuality 8 

Description 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

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This value expresses the percentage of all detected minutiae quality values that are 

in the range from 71 until 80 (Absolute number of minutiae quality values that are >= 

71 and <= 80 divided by the absolute number of detected minutiae). 

2.2.13 Minutiae Quality 9 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_9 

identifier 

Also known as MinutiaeQuality 9 

Description 

This value expresses the percentage of all detected minutiae quality values that are 

in the range from 81 until 90 (Absolute number of minutiae quality values that are >= 

81 and <= 90 divided by the absolute number of detected minutiae). 

2.2.14 Minutiae Quality 10 

Origin DigitalPersona FingerJetFX OSE 

NFIQ2.0 FingerJetFX_MinutiaeQuality_10 

identifier 

Also known as MinutiaeQuality 10 

Description 

This value expresses the percentage of all detected minutiae quality values that are 

in the range from 91 until 100 (Absolute number of minutiae quality values that are 

>= 91 and <= 100 divided by the absolute number of detected minutiae). 

2.3 hda Features 

2.3.1 Notation 

Image width in pixels (horizontal) 

Image height in pixels (vertical) 

Image location where denotes the pixel in the upper left corner 

Number of blocks horizontally 

Number of blocks vertically 

Image block where denotes the block in the upper left corner 

2.3.2 Frequency Domain Analysis 

Origin ISO/IEC TR 29794-4:2010 (ISO/IEC, 2010) – Clause 6.2.2.3 

NFIQ2.0 identifier FDA_# 

Short acronym 

Description 

The Frequency Domain Analysis algorithm operates in a block-wise manner. A 

signature of the ridge-valley structure is extracted and the DFT is computed to 

determine the frequency of the sinusoid following the ridge-valley structure. 

Extracting the ridge-valley signature 

Computing the Frequency Domain Analysis score 

Algorithm 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

1. For each block determine the dominant ridge flow orientation 

2. Rotate the block such that the dominant ridge flow is perpendicular to the x-axis 

3. Crop regions of block such that no invalid regions are included in the block 

4. Calculate the mean pixel intensity value

11 

for the block to extract the ridge-valley 

structure 

5. Calculate the Fourier spectrum of 

6. Discard the DC component of and determine the term with the highest magnitude 

7. The final Frequency Domain Analysis score is the mean of scores assigned to foreground 

blocks. 

Notes 

The value of is undefined if or as is not a valid 

index. Workaround in that case is to set . 

Processing 

Input 

Input with block grid and current block marked in blue 

Zoomed view of current block 

Block rotated
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

12 

Block cropped 

Block mean intensity values 

Block DFT 

Map of FDA quality values 

2.3.3 Gabor 

Origin Olsen, Xu, Busch, Gabor Filters as Candidate Quality Measure for NFIQ 2.0 

in ICB 2012 

NFIQ2.0 identifier GABOR_# 

Short acronym GAB 

Description 

The Gabor quality metric operates on a per-pixel basis by calculating the standard 

deviation of the Gabor filter bank responses. The size of the filter bank is used to 

determine a number of filters oriented evenly across the half circle. The strength of 

the response at a given location corresponds agreement between filter orientation 

and frequency in the location neighborhood. For areas in the fingerprint image with a 

regular ridge-valley pattern there will be a high response from one or a few filter 

orientations. In areas containing background or unclear ridge-valley structure the 

Gabor response of all orientations will be low and constant. 

Variables 
For a block there are pixels in the valley region and pixels in the valley 

region with intensity lower than a threshold . Similarly there are pixels in the 

ridge region and pixels in the ridge region with intensity lower than a threshold . 

 and are expressions of these ratios. 

The normalized valley width and the normalized ridge width are determined 

where is the scanner resolution in dpi, is the estimated ridge or valley width 

for an image with 125 dpi resolution, and and are the observed valley and 

ridge widths. According to [] is reasonable for 125 dpi resolution. 

Computing the Local Clarity Score

The final quality score is computed using the average value of and in valid ridge 

and valley regions:

 and are the minimum values for the normalized ridge and valley 

 and are the maximum values for the normalized ridge and 

where

width, and 

valley width. 

Algorithm

1. For each block in the image determine the dominant ridge flow orientation to create an 

orientation line which is perpendicular to the ridge flow 

2. Align such that the orientation line is horizontal to create 

3. From extract a block which is centered around the orientation line 

4. Compute the average profile of 

5. Determine a threshold by applying linear regression on 

6. Determine the proportion of misclassified pixels and in the ridge and valley regions

7. Determine the normalized ridge width and valley width and . 

8. Compute the final quality score . 

Further Comments 

Particular regions inherent in a fingerprint will negatively affect . For example, 

ridge endings and bifurcations or areas with high curvature such as those commonly 

found in core and delta points. 

Processing 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

14 

Input (FVC2000 Db1A 1_1.bmp) 

Subtraction of image convolved with Gaussian. 

Gabor response for filtered image at orientation 0 

Gabor response for filtered image at orientation 1/4 

Gabor response for filtered image at orientation 2/4 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

15 

Gabor response for filtered image at orientation 3/4 

Gaussian filtered Gabor response for filtered image at orientation 3/4 

Standard deviation of Gaussian filtered responses 

2.3.4 Gabor Segment 

Origin 

NFIQ2.0 identifier GS_# 

Short acronym and GSG, GaborSeg 

alternate identifier 

Description 

Same as Gabor with the exception that the image is initially convolved with a 2D 

Gaussian kernel with instead of . Additionally a segmentation to 2 levels 

is applied before computing the final quality score. 

Segment to two levels 

Segmenting the map of standard deviations into two levels is done by first 

determining the cumulative distribution function for pixel intensity values. Next a 

threshold is determined such that the probability of a pixel belonging to background is 

the same as that for belonging to the foreground. 

Algorithm 

1. Convolve input image with a 2D Gaussian kernel with

2. Compute the Gabor response of the image for each orientation 

3. Convolve the magnitude (complex modulus) of each Gabor response with a 2D Gaussian 

kernel with

4. Compute the standard deviation of the Gabor magnitude response values at each location 

yielding a map of standard deviations. 

5. Segment the standard deviation map into two levels. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

13 

2D Gaussian standard deviation in x-direction 

2D Gaussian standard deviation in y-direction 

Size of filter bank , orientations of the gabor wave 

D Gabor filter frequency 

 !	"

The general form of the complex 2D Gabor(Daugman, 1985) filter in the spatial 

domain is given by: 

where 

and f is the frequency (cycles/pixel) of the sinusoidal plane wave along the 

orientation . The size of the Gaussian smoothing window is determined by . 

The filter bank size n is used to compute the differently oriented Gabor filters 

composing the filter bank. The computation of given n is as follows: 

	

es VrdbrNbpDhdFmaDhgt-pDlhanDtDySDBtmCChtdDEpidpNDlhan D DtdxDCm aitoaDhaDvirgDanpDhdFmaD

hgt-pDarD-hbpDD

ys VrgFmapDanpDBt riDipCFrdCpDrvDDvriDptonDrihpdatahrd D D

Ps VrdbrNbpD anpD gt-dhamxpD 2orgFNpqD grxmNmCMD rvD ptonD Bt riD ipCFrdCpD lhanD tD ySD BtmCChtd

EpidpNDlhanD D

Ts VrgFmapDanpDCatdxtixDxpbhtahrdDrvDanpDBt riDgt-dhamxpDipCFrdCpDbtNmpCDtaDptonDNrotahrd

GhpNxhd-DtDgtFDrvDCatdxtixDxpbhtahrdCsD

fs ,mgD anpDgtFD rvD CatdxtixD xpbhtahrdCD tdxD drigtNh-pDtoorixhd-D arD dmg piDrvD CtgFNpD FrhdaCD

2aGFhotNNGDhgt-pDCh-pMDarDFirxmopDanpDvhdtNDBt riDzmtNhaGDCoripsD

#		


For 500ppi images the following settings are reasonable: 

	


Real part of Gabor filter Imaginary part of Gabor filter 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

17 

Input 

Gabor response at orientation 0 

Gabor response at orientation 1/4 

Gabor response at orientation 2/4 

Gabor response at orientation 3/4 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

18 

Standard deviation of filter responses 

Blockwise mean of filter resposes 

Foreground blocks 

Blocks marked as poor 

2.3.6 Local Clarity Score 

Origin ISO/IEC TR 29794-4:2010 (ISO/IEC, 2010) – Clause 6.2.2.2 

NFIQ2.0 identifier LCS_# 

Short acronym and LCS, Ridge-valley Structure 

alternate identifier 

Description 

Local Clarity Score (LCS) computes the block wise clarity of ridge and valleys by 

applying linear regression to determine a gray-level threshold, classifying pixels as 

ridge or valley. A ratio of misclassified pixels is determined by comparing with the 

normalized ridge and valley width of that block. 

Computing the average profile of a block 

Given the block the average profile is obtained by 

where is the height of the block. 

Determining the proportion of misclassified pixels 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

For a block

19 

 there are pixels in the valley region and pixels in the valley 

region with intensity lower than a threshold . Similarly there are pixels in the 

ridge region and pixels in the ridge region with intensity lower than a threshold . 

and are expressions of these ratios. 

Determining the normalized ridge and valley width 

The normalized valley width and the normalized ridge width are determined 

where is the scanner resolution in dpi, is the estimated ridge or valley width 

for an image with 125 dpi resolution, and and are the observed valley and 

ridge widths. According to [] is reasonable for 125 dpi resolution. 

Computing the Local Clarity Score 

The final quality score is computed using the average value of and in valid ridge 

and valley regions:

where and are the minimum values for the normalized ridge and valley 

width, and and are the maximum values for the normalized ridge and 

valley width. 

Algorithm 

1. For each block in the image determine the dominant ridge flow orientation to create an 

orientation line which is perpendicular to the ridge flow 

2. Align such that the orientation line is horizontal to create 

3. From extract a block which is centered around the orientation line 

4. Compute the average profile of 

5. Determine a threshold by applying linear regression on

6. Determine the proportion of misclassified pixels and in the ridge and valley regions 

7. Determine the normalized ridge width and valley width and . 

8. Compute the final quality score .

Further Comments 

Particular regions inherent in a fingerprint will negatively affect . For example, 

ridge endings and bifurcations or areas with high curvature such as those commonly 

found in core and delta points. 

Processing 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

20 

Input 

Input with grid and block marked in blue 

Zoomed view of block 

Block rotated to align ridgelines vertically 

Extracted section of the block 

Projected mean gray intensity values 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

21 

Ridge-valley profile as found by mean values. Red line indicates the threshold for ridge-valley separation. 

Ridge-valley segmentation. Ridges shown in black, valleys in white. The mean intensity values are shown above for 

comparison. 

Ridge areas composed into one block. 

Shown in white are the parts of the fingerprint which were determined to be ridge but do not belong to ridge as defined by the 

threshold. 

Valley areas composed into one block. 

Shown in white are the parts of the fingerprint which were determined to be valley but do not belong to valley as defined by the 

threshold. 

Map of local clarity scores. High intensity corresponds to high local clarity. 

2.3.7 Mu 

Origin 

NFIQ2.0 identifier Mu_# 

Short acronym MUQ 

Description 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

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Mu is the mean pixel intensity value in the input image. 

Algorithm 

2.3.8 Mu Mu Block 

Origin 

NFIQ2.0 identifier MMB_# 

Short acronym MMB 

Description 

Mu Mu Block is the mean of the block wise mean pixel intensity value in the input 

image. 

2.3.9 Mu Mu Sigma Block 

Origin 

NFIQ2.0 identifier MMSB_# 

Short acronym MMSB 

Description 

Mu Mu Sigma Block is the mean of the block wise standard deviation pixel intensity 

value in the input image subtracted the block wise standard deviation. 

2.3.10 Mu Sigma Block 

Origin 

NFIQ2.0 identifier MSB_# 

Short acronym MSB 

Description 

Mu Sigma Block is the mean of the block wise standard deviation pixel intensity value 

in the input image. 

2.3.11 Orientation Certainty Level 

Origin ISO/IEC TR 29794-4:2010 (ISO/IEC, 2010) - Clause 6.2.2.1 

NFIQ2.0 identifier OCL_# 

Short acronym OCL 

Description 

Orientation Certainty Level is a measure of the strength of the energy concentration 

along the dominant ridge flow orientation. The metric operates in a block-wise 

manner. 

Computing the covariance matrix 

The covariance matrix is computed as: 

where and represent the intensity gradient at that pixel. 

Computing the eigenvalues and the final quality score 

From the covariance matrix the eigenvalues and are computed as: 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

23 

this yields an orientation certainty level OCL: 

which is a ratio in the interval where 1 is highest certainty level and 0 is lowest. 

Algorithm 

For each block

1. Compute the intensity gradient by applying the 3x3 Sobel operators 

2. Compute the covariance matrix 

3. Compute the eigenvalues to obtain 

Finally compute the quality measure as: 

Processing 

Input 

Input with current block marked 

Zoomed view of block 

Ridge orientation indicated as an ellipse. The eccentricity of the ellipse corresponds to the orientation certainty. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

24 

Orientation certainty level in each block. High intensity corresponds to high level of certainty. 

2.3.12 Orientation Flow 

Origin ISO/IEC TR 29794-4:2010 (ISO/IEC, 2010) - Clause 6.3.2.1 

NFIQ2.0 identifier OF_# 

Short acronym OF 

Description 

Orientation Flow is a measure of ridge flow continuity which is based on the absolute 

orientation difference between a block and its neighboring blocks. 

Block-wise absolute orientation difference 

The ridge flow is determined as a measure of the absolute difference between a 

block and its neighboring blocks. The absolute difference for block is: 

Local orientation quality score 

The local orientation quality score for the block orientation difference 

is determined as: 

where is a threshold for minimum angle difference to consider. 

Global orientation quality score 

With local orientation quality score blocks the global orientation quality score is 

computed as: 

Algorithm 

1. Compute the absolute orientation difference for each block

2. Compute the local orientation quality score for 

3. Compute the global orientation flow quality score 

Processing 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

25 

input 

Current block marked 

Zoomed view of current block 

Orientation line shown perpendicular to ridges. 

Block orientations 

Map of differences between a block orientation and its 8-neighborhood 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

26 

Final flow map 

2.3.13 Radial Power Spectrum 

Origin ISO/IEC TR 29794-4:2010 (ISO/IEC, 2010) - Clause 6.3.2.3 

NFIQ2.0 identifier PS_# 

Short acronym and RPS, POW, Radial Power Spectrum 

alternate identifier 

Description 

The Radial Power Spectrum is a measure of maximal signal power in a defined 

frequency band of the global radial Fourier spectrum. Ridges can be locally 

approximated by means of a single sine wave, hence high energy concentration a 

narrow frequency band corresponds to consistent ridge structures. 

Variables 

Lower bound of frequency band 

Upper bound of frequency band 

Frequency bin width 

The 2D Discrete Fourier Transform 

The 2D discrete Fourier transform of is: 

and the magnitude of is: 

Magnitude of frequency bands polar coordinates 

The magnitude of the annular band between and in the polar Fourier spectrum 

 is computed as: 

where is the angle and is the radius. 

Determine quality score from energy distribution 

The quality metric is found as: 

Algorithm 

1. Compute the magnitude of the 2D-DFT 

2. Transform into polar coordinates and normalize to the range of 

3. Determine the maximum energy to compute 

Process 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

27 

Input 

Spectrum with the frequency band of interest located between the 

Polar representation of the spectrum 

Spectrum with marked frequency of interest 

2.3.14 Ridge Valley Uniformity 

Origin ISO/IEC TR 29794-4:2010 (ISO/IEC, 2010) - Clause 6.2.2.4 

NFIQ2.0 identifier RVU_# 

Short acronym RVU 

Description 

Ridge Valley Uniformity is a measure of the consistency of the ridge and valley 

widths. 

Algorithm 

1. For each block in the image determine the dominant ridge flow orientation to create an 

orientation line which is perpendicular to the ridge flow 

2. Align such that the orientation line is horizontal to create 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

28 

3. From extract a block which is centered around the orientation line 

4. Compute the average profile of to produce a vector 

5. Determine a threshold by applying linear regression on

6. Segment into two levels based on the threshold 

7. Determine the indexes in where a change from background to foreground or foreground 

to background occurs. If no changes are found then return an empty ratio for that block. 

8. Remove the first and last parts of to remove incomplete ridge/valleys occurring at the 

border of the original block. Likewise remove the corresponding changes from the change 

index vector. 

9. If there are no changes after step 8, return an empty ratio for that block 

10. Calculate the ratios between the width of ridges and valleys for the block. 

11. Obtain the final quality score as the standard deviation of all ratios. 

Process 

Input 

Input with block grid and current block marked 

Current block 

Current block with overlap 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

29 

Block rotated so ridge lines are vertical 

Cropped section of block 

Projection of mean intensity 

Segmentation according to thresholding. Ridges marked in black, valleys in white 

Location of ridge-valley edges marked with white 

Cleaned ridge-valley segmentation contains only complete ridges and valleys. 

2.3.15 Sigma 

Origin 

NFIQ2.0 identifier Sigma_# 

Short acronym SIG 

Description 

Sigma is the standard deviation of pixel intensity values in the input image. 

Algorithm 

where is the mean pixel intensity of . 

2.3.16 Sigma Mu Block 

Origin 

NFIQ2.0 identifier SMB_# 

Short acronym SMB 

Description 

Sigma Mu Block is the standard deviation of the block wise mean pixel intensity value 

in the input image. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

30 

3 Evaluation Approach 

3.1 Methodology 

The following methods were used to evaluate the features: 

 Determination of spearman correlation among the features and with a utility 

measure determined from a large number of biometric comparisons. 

 Visual inspection of scatter plots. 

 Computation of ROC curves resulting from a biometric application simulation, 

where the feature values are used to select the best fingerprint during enrolment. 

Details are described in Section 3.1.1. 

 Computation of ERC showing the dependence of the FNMR from filtering the 

genuine scores by the feature values of both reference and probe images (see 

Section 3.1.3). 

3.1.1 Spearman Correlation and Scatter Plots 

We compute the spearman correlation, i.e. rank correlation, of the feature values with 

both the utility values and with values of other features. The utility values are 

computed according to ISO/IEC 29794-1 for providers 28, 63 and 83, and are used 

as “ground truth” metric for quality, i.e. the correlation coefficients of the feature 

values with utility are supposed to indicate their dependency on quality and viceversa. The correlation coefficients with other feature’s values indicate the potential 

redundancy among the different features. 

For the ease of reading, the correlation matrices are colored according to the 

absolute value of the correlation coefficient (darker color for higher absolute values) 

and the coefficients are shown multiplied by 100 and rounded. 

In addition to correlation, scatter plots are computed with both the utility values and 

with values of other features. The scatter plots allow detection of more comlex 

dependencies than the (one dimensional) correlation. 

3.1.2 Evaluation by ROC Curves 

In order to evaluate how indicative a feature is for biometric performance, we 

simulate a biometric application using the feature values as quality assessment 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

31 

criteria. In particular, we perform the following steps on a large database of 

fingerprints with several impressions per finger: 

1. For each fingerprint, we compute a large number of genuine and impostor 

comparison scores. 

2. For each finger, we select the impression having the highest feature value as 

reference fingerprint. 

3. For the selected reference fingerprints, average error rates are computed from 

their similarity scores. In particular, from the comparison scores of the selected 

fingerprints accumulated genuine and impostor score distributions are computed 

and ROC curves are determined. The smaller the error rates are, the more 

eligible the utility function is to perform quality assurance with the deployed filter 

method and threshold. The probe fingerprints are not filtered by the feature 

values. 

4. Steps 2 and 3 are repeated but this time, in step 2, the impression having the 

lowest feature value is chosen as reference image. 

5. The better of the two resulting ROC curves, i.e. the ROC curve with lowest error 

rates is chosen as a measure for the feature. 

These steps simulate an evaluation of biometric performance of a biometric system 

which deploys the utility function for quality assessment during enrolment. 

The ROC curves were computed from the comparison scores of several providers. 

For comparison, ROC curves were also computed for the case where always the first 

of the three impressions (with respect to their acquisition numbers) is chosen as 

reference fingerprint. Since the acquisition number is assumed to be unrelated to 

image quality, this selection criterion simulates a scenario where no quality filtering is 

applied at all. 

3.1.3 ERC Evaluation 

An error reject curve (ERC) shows the dependence of the FNMR at a fixed 

comparison score threshold on the extent of filtering the genuine scores by feature 

value of both reference and probe image. First, a comparison score threshold t is 

selected which results in a fixed FNMR (e.g. 10%), e.g. for which a fraction of FNMR 

genuine scores are equal or larger than t. Then for each feature threshold 0 ≤ q ≤ 1, 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

32 

all genuine scores are neglected for which the feature value of the reference image 

or the feature value of the probe image is below the threshold q. For the remaining 

genuine scores, the fraction of genuine scores equal or larger than t is determined, 

e.g. the FNMR resulting from filtering a q fraction of the genuine scores. 

The resulting ERC can vary for different FNMR. Thus, we compute ERC for FNMR of 

3% and FNMTR of 10%. 

3.2 Data basis 

For our experiments we use the FingerQS database of BSI, which contains 9 

impressions (taken with 3 sensors) of 8784 fingers from 1098 individuals (all fingers 

except little fingers). In order to amplify differences between tested utility functions in 

the resulting error rates, we split each user-ID into 3 pseudo-IDs and assign for each 

finger code 3 fingerprints of this user-ID (taken by the same sensor) to each pseudoID. Thus, we treat the impressions of each finger as impressions of 3 different 

fingers. By this re-labeling, the number of user-IDs is increased by a factor of three 

and, at the same time, the number of fingerprints per finger is reduced by a factor of 

three; the reduction of impressions per finger increases the impact of variations in the 

utility function to the selection of the best fingerprint per finger. 

The utility values are computed for three different providers (SDKs), having identifiers 

28, 63 and 83. For each fingerprint and each provider, 8 genuine scores and, on 

average, 57.5 impostor scores are used to compute the utility values. ROC curves 

are also computed for all three providers. In contrast, we restrict our evaluation with 

ERC computed from the comparison scores of one provider (28). 

3.3 Limitations 

Both the correlation coefficients and the ROC curves measure to what extend the 

utility increases or decreases with feature values. Any non-monotonic dependency of 

feature values from utility is not captured. However, such monotonic dependency can 

be visible in the ERC and in the scatter plots. 

Furthermore, since in the ROC curve method the filtering of fingerprints is performed 

per finger but not between impressions of different fingers, only intra-finger 

dependencies of feature values from utility are measured. Therefore, only influences 

of the quality aspect “fidelity” as defined in ISO/IEC 29794-1 [6], [12], i.e. the quality 

of the image with respect to the original characteristic, on the feature are captured, 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

33 

and the impact of the quality aspect “character”, i.e. the eligibility of the character for 

biometric applications, can not be determined from the ROC curves. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

34 

4 Evaluation Results 

4.1 Evaluation of the NFIQ1-Features 

4.1.1.1 Correlation and Scatter Plots 

The spearman correlation coefficients of the 11 NFIQ1 features are given in the 

following table. 

foreground

foreground

NoMinutiae

100 46

min05min06

47 47

min075

42 34min0826min09-19QualZone1-43 -4 QualZone2 QualZone325 16 QualZone4 util282 9 util63 util83

NoMinutiae

min05

min06

min075

min08

min09

QualZone1

QualZone2

QualZone3

QualZone4

46 100

47 49

47 49

42 50

34 46

26 35

-19 20

-43 18

-4 24

25 -24

49 49

100 99

99 100

93 95

76 79

54 56

-46 -45

-47 -47

-47 -48

58 58

50 46

93 76

95 79

100 90

90 100

69 87

-36 -25

-42 -32

-45 -37

52 41

35

54

56

69

87

100

-20

-20

-31

31

20

-46

-45

-36

-25

-20

100

52

66

-85

18 24

-47 -47

-47 -48

-42 -45

-32 -37

-20 -31

52 66

100 39

39 100

-75 -86

-24 -7

58 33

58 33

52 31

41 28

31 23

-85 -34

-75 -31

-86 -40

100 44

-7 -6

21 26

21 26

21 26

19 24

16 20

-22 -27

-18 -25

-27 -31

29 35

util28 16 -7 33 33 31 28 23 -34 -31 -40 44 100 80 83

util63 2 -7 21 21

26 26

21 19

26 24

16

20

-22

-27

-18 -27

-25 -31

29 80

35 83

100 82

util83 9 -6 82 100

Table 2: Spearman correlation coefficients for NFIQ1 features 

The highest correlation with utility are observed for (in that order) 

1. Quality Zone 4, 

2. Quality Zone 3, 

3. Quality Zone 1, 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

35 

4. min05 and min06, 

5. min075. 

Scatter plots of the features with provider 63 utility according to ISO 28784-1 [5] and 

density plots of the empirical feature distributions are shown in Appendix A.1. 

4.1.1.2 ROC curve evaluation 

The following table shows for each NFIQ1 feature, which selection criterion, i.e. 

choosing the impression with highest feature value or selecting the one with lowest 

feature value, results in better a ROC curve. 

Feature Abbr. Criterion

foreground FG lowest 

total # of minutiae NoMin lowest 

min05 Min05 highest 

min06 Min06 highest 

min075 Min075 highest 

min08 Min08 highest 

min09 Min09 lowest 

quality zone 1 QZ1 lowest 

quality zone 2 QZ2 lowest 

quality zone 3 QZ3 lowest 

quality zone 4 QZ4 highest 

Table 3: Optimal selection criteria for ROC curves 

For these criteria, the ROC curves are shown in Figure 2, Figure 3 and Figure 4 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

36 

Figure 2: ROC curves of NFIQ1 features for provider 28 

Figure 3: ROC curves of NFIQ1 features for provider 63 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

37 

Figure 4: ROC curves of NFIQ1 features for provider 83 

The following observations were made: 

 Consistently, quality zone 4 performed best, followed by quality zone 3, total # of 

minutiae and foreground. 

 Although feature min08 is positively correlated with utility, the ROC curves for 

both selection criteria were worse than that of a random selection (NULL feature). 

This finding indicates, that good quality fingerprint neither have particularly high 

nor particularly low feature values. 

 For provider 83, feature quality zone 1 also performed worse than no quality 

filtering, but still choosing the impression with lowest value gave better results 

than selecting the one with the highest value. 

Scatter plots of the features with provider 63 utility according to ISO 28784-1 [5] and 

density plots of the empirical feature distributions are shown in Appendix A.1. 

4.1.1.3 ERC Evaluation 

For the NFIQ1 features the following ERC curves were obtained. The dotted diagonal 

line from (0.1, 0) to (0, 0.1) for 10% FNMR and from (0.03,0) to (0.03,0) for 3% 

FNMR shows the ideal (and hypothetical) ERC in case of a perfect quality filtering, 

where scores are rejected according to their quality, starting with the lowest quality 

value. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

38 

Figure 5: ERC of NFIQ 1 features at 3% FNMR for provider 28 

Figure 6: ERC of NFIQ 1 features at 10% FNMR for provider 28 

4.2 Evaluation of the FingerJetFX Features 

It turned out that some of the FingerJetFX features always returned the same value: 

 The fingerprint quality is hard coded in the source code to the value 86. 

 No minutiae with quality value between 0 and 0.4 were detected. 

The spearman correlation matrix is shown below. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

39 

AvMinQual 100

AvMinQual

-38MinCount-5MinQual4-28MinQual5-77MinQual6-42MinQual753MinQual885MinQual976MinQual1030util2817util6321util83

MinCount -38 100 -29 -45 31 59 -8 -53 -45 -13 -7 -9

MinQual4 -5 -29 100 29 -2 -20 -7 7 8 1 1 1

MinQual5 -28 -45 29 100 19 -52 -35 5 10 5 7 6

MinQual6 -77 31 -2 19 100 14 -56 -57 -45 -8 0 -2

MinQual7 -42 59 -20 -52 14 100 -2 -66 -62 -32 -21 -23

MinQual8 53 -8 -7 -35 -56 -2 100 28 15 9 3 7

MinQual9 85 -53 7 5 -57 -66 28 100 72 36 22 27

MinQual10 76 -45 8 10 -45 -62 15 72 100 32 21 24

util28 30 -13 1 5 -8 -32 9 36 32 100 80 83

util63 17 -7 1 7 0 -21 3 22 21 80 100 82

util83 21 -9 1 6 -2 -23 7 27 24 83 82 100

Table 4: Spearman correlation coefficients for NFIQ1 features 

The highest correlation with utility was observed (in that order) for 

1. Minutiae Quality 9 

2. Minutiae Quality 10 

3. Minutiae Quality 7 

4. Average Minutiae Quality 

Scatter plots of the features with provider 63 utility according to ISO 28784-1 [5] and 

density plots of the empirical feature distributions are shown in Appendix A.1. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

40 

4.2.1.1 ROC Curve Evaluation 

The following table shows for the remaining FingerJetFX features, which selection 

criterion, i.e. choosing the impression with highest feature value or selecting the one 

with lowest feature value, results in better a ROC curve. 

Feature Abbr. Criterion

AverageMinutiaeQuality AMQ lowest 

MinutiaeCount MinCount lowest 

MinutiaeQuality_4 MQ4 highest 

MinutiaeQuality_5 MQ5 highest 

MinutiaeQuality_6 MQ6 highest 

MinutiaeQuality_7 MQ7 lowest 

MinutiaeQuality_8 MQ8 lowest 

MinutiaeQuality_9 MQ9 highest 

MinutiaeQuality_10 MQ10 highest 

Table 5: Optimal selecton criteria for ROC curves 

For these criteria, the ROC curves are shown in Figures 

Figure 7: ROC curves of FingerJet FX features for provider 28 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

41 

Figure 8: ROC curves of FingerJet FX features for provider 63 

Figure 9: ROC curves of FingerJet FX features for provider 83 

The following observations were made: 

 Minutiae Quality 7 took ranked first for all providers. 

 For provider 63 and 83, Minutiae Quality 9 and Minutiae Quality 6 took the 

second and third place, for provider 28 these ranks were exchanged. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

 Consistently, Minutiae Count, Minutiae Quality 4, and Minutiae Quality 8 showed 

very poor performance. 

4.2.1.2 ERC Evaluation 

For the FingerJet FX features the following ERC curves were obtained for FNMR of 

3% and 10%. Again, the dotted diagonal line from (0.1, 0) to (0, 0.1) for 10% FNMR 

and from (0.03,0) to (0.03,0) for 3% FNMR shows the ideal (and hypothetical) ERC in 

case of a perfect quality filtering, where scores are rejected according to their quality, 

starting with the lowest quality value. 

42 

Figure 10: ERC of FingerJetFX features at 3% FNMR for provider 28 

Figure 11: ERC of FingerJetFX features at 10% FNMR for provider 28 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

43 

4.3 Evaluation of the Features Implemented by hda 

For each of the features implemented by hda (see Section 2.3), 13 configurations 

were tested, resulting in 208 different feature instances. The following Table shows 

the configurations used. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

44 

Config0 Config1 Config2 Config3 Config4 Config5 Config6 Config7 Config8 Config9 

Config10 Config11 Config12 

segmentBlocksizeValue 32 16 16 16 32 32 32 16 32 16 32 32 16 

segmentTresholdValue 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

qualityBlocksizeValue 32 16 16 16 32 32 32 16 32 16 32 32 16 

orientationFlowBorderValue 1 1 1 1 1 1 1 1 1 1 1 1 1 

orientationFlowAngminValue 0 0 4 -4 0 4 -4 0 -4 0 4 0 -4 

slantedBlocksizeValue 32,16 16,8 16,8 16,8 32,16 32,16 32,16 16,8 32,16 16,8 32,16 32,16 16,8 

powerNradValue 30 20 30 40 20 30 20 40 30 30 30 40 40 

powerNthetaValue 180 180 180 180 180 180 180 180 180 180 180 180 180 

powerRminValue 25 20 25 30 20 25 20 30 25 30 20 30 30 

powerNRmaxValue 84 64 84 104 64 64 104 84 64 64 104 64 64 

gaborAnglesValue 8 4 6 8 4 6 8 6 4 8 6 5 5 

gaborFreqValue 0.1 0.05 0.1 0.15 0.05 0.1 0.05 0.1 0.15 0.15 0.05 0.15 0.15 

gaborSigmaValue 6 5 6 7 5 6 5 5 7 5 7 5 5 

gaborShenTbValue 1 0.5 1 1.2 0.5 1 1.2 0.5 1.2 1.2 0.5 1.2 1.2 

gaborShenTqValue 2 1.8 2 2.5 1.8 2 2.5 1.8 1.8 2.5 2.5 1.8 1.8 

Table 6: Configurations used for feature computation 

It turned out, that many configurations of a feature gave equivalent values, i.e. with 

spearman correlation of 1, or almost identical values resulting in a very high 

correlation. Furthermore, for all configurations, sigmaMuBlock and 

SigmaMuSigmaBlock gave almost equivalent values to MuMuSigmaBlock 

(correlation coefficient of -0.99). As a consequence, only 59 distinct instances 

(feature + configuration) remained. 

Config 1 2 3 4 5 6 7 8 9 10 11 12 

FDA equiv1 equiv1 equiv0 equiv0 equiv1 equiv1 equiv0 equiv0 equiv0 equiv1 

gabor almost1 equiv11

GS equiv1 equiv11

GSh equiv0 equiv1 equiv3 equiv3 equiv9 equiv9 

LCS equiv1 equiv1 equiv0 equiv7 equiv0 equiv0 equiv0 equiv1 

mu Equiv0 equiv0 equiv0 equiv0 equiv0 equiv0 equiv0 equiv0 equiv0 equiv0 equiv0 equiv0 

MMB equiv1 equiv1 almost0 equiv0 equiv0 almost1 equiv7 equiv0 equiv0 equiv0 equiv1 

MMSB equiv1 equiv1 almost0 equiv0 equiv0 almost1 equiv7 equiv0 equiv0 equiv0 equiv1 

MSB equiv1 equiv1 almost0 equiv0 almost1 equiv7 equiv0 equiv0 equiv0 equiv1 

OCL equiv1 equiv1 equiv0 equiv0 equiv0 equiv1 equiv1 equiv0 equiv0 equiv0 equiv1 

OF equiv1 equiv0 equiv0 equiv1 equiv1 equiv6 equiv5 equiv0 equiv3 

PS equiv0 const0 equiv1 const0 const1 const0 const0 const0 const0 const0 

RVU equiv1 equiv1 equiv4 equiv0 equiv7 equiv0 equiv0 equiv0 equiv1 

sigma Equiv0 equiv0 equiv0 almost0 equiv0 equiv0 almost0 equiv7 equiv0 equiv0 equiv0 equiv0 

SSB equiv1 equiv1 almost0 equiv0 equiv0 almost1 equiv7 equiv0 equiv0 equiv0 equiv1 

Table 7: Equivalent, almost identical or constant values of the feature configurations 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

45 

Furthermore, we did not consider configurations of a feature that exhibited high 

correlations (greater than 0.875) with other configurations of the same feature. Of the 

remaining, i.e. the non-redundant, feature configurations, the following showed 

relatively (for the feature) good biometric performance in the ROC curve evaluation. 

Config 0 1 2 5 7 9 10 

FDA x

gabor x

GS x x

GSh x

LCS x X

mu x

MMB x x

MMSB x

MSB x

OCL x

OF x

PS x

RVU x x

sigma x

SMB 

SSB x

 Table 8: Non-redundant configurations giving relatively good performance for the feature 

4.3.1.1 Correlation and scatter plots 

For these feature, the correlation coefficients are given in the following table. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

46 

FDA_1

FDA_1

100 -32 24 -35 -6 -36 45 42 69 13 -41 17 81 23 -38 -32 -25 40 1 1 4 gabor_0 GS_5 GS_9 GSh_10 LCS_1 LCS_7 mu_0 MMB_0 MMSB_0 MSB_0 OCL_0 OF_2 PS_0 RVU_0 RVU_1 sigma_0 SSB_1 util28 util63 util83

gabor_0 -32 100 19 66 89 86 61 -77 -4 13 64 73 -8 66 -38 -37 78 -43 31 22 24

GS_5 24 19 100 12 20 11 32 -22 14 21 20 27 28 14 -22 -20 16 -18 2 2 2

GS_9 -35 66 12 100 47 70 35 -73 0 24 70 37 -27 33 -8 -9 62 -51 2 0 -1

GSh_10 -6 89 20 47 100 66 72 -57 19 6 37 84 24 76 -55 -52 70 -26 41 30 34

LCS_1 -36 86 11 70 66 100 57 -79 -12 14 72 63 -23 43 -25 -27 77 -47 14 12 11

LCS_7 45 61 32 35 72 57 100 -41 56 13 23 88 58 70 -64 -57 57 -16 29 22 25

mu_0 42 -77 -22 -73 -57 -79 -41 100 5 16 -52 -52 24 -37 -6 -7 -93 85 -2 -1 -2

MMB_0 69 -4 14 0 19 -12 56 5 100 0 -28 36 70 35 -31 -24 12 4 10 7 11

MMSB_0 13 13 21 24 6 14 13 16 0 100 65 0 -3 13 -42 -47 -27 49 1 -2 -4

MSB_0 -41 64 20 70 37 72 23 -52 -28 65 100 24 -43 25 -19 -23 33 -18 3 0 -2

OCL_0 17 73 27 37 84 63 88 -52 36 0 24 100 49 65 -59 -55 69 -25 34 27 30

OF_2 81 -8 28 -27 24 -23 58 24 70 -3 -43 49 100 34 -51 -44 -3 25 19 15 18

PS_0 23 66 14 33 76 43 70 -37 35 13 25 65 34 100 -48 -39 49 -11 36 24 28

RVU_0 -38 -38 -22 -8 -55 -25 -64 -6 -31 -42 -19 -59 -51 -48 100 86 -7 -35 -39 -28 -31

RVU_1 -32 -37 -20 -9 -52 -27 -57 -7 -24 -47 -23 -55 -44 -39 86 100 -5 -40 -34 -26 -27

sigma_0 -25 78 16 62 70 77 57 -93 12 -27 33 69 -3 49 -7 -5 100 -76 9 8 10

SSB_1 40 -43 -18 -51 -26 -47 -16 85 4 49 -18 -25 25 -11 -35 -40 -76 100 8 6 5

util28 1 31 2 2 41 14 29 -2 10 1 3 34 19 36 -39 -34 9 8 100 80 83

util63 1 22 2 0 30 12 22 -1 7 -2 0 27 15 24 -28 -26 8 6 80 100 82

util83 4 24 2 -1 34 11 25 -2 11 -4 -2 30 18 28 -31 -27 10 5 83 82 100

Table 9: Spearman correlation coefficients for hda features 

The highest correlation with utility was observed (in that order) for 

1. Gabor Shen 

2. Ridge Valley Uniformity (config 0) 

3. Power Spectrum 

4. Orientation Certainty Level 

5. Ridge Valley Uniformity (config 1) 

6. Local Clarity Score (config 7) 

Scatter plots of the features with provider 63 utility according to ISO 28784-1 [5] and 

density plots of the empirical feature distributions are shown in Appendix A.1. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

47 

4.3.1.2 ROC Curve Evaluation 

For some features, the optimal selection criterion, i.e. if choosing the impression with 

highest feature value or selecting the one with lowest feature value, results in better 

ROC curves, varied among the configurations. However, for the better performing 

configurations of each feature, the criterion was consistent. The following table 

shows the optimal selection criterion for the hda features. 

Feature Abbr. Criterion

Frequency Domain Analysis FDA highest 

Gabor gabor Highest 

Gabor Segment 5 GS5 Lowest 

Gabor Segment 9 GS9 Lowest 

Gabor Shen GSh Highest 

Local Clarity Score 1 LCS1 Highest 

Local Clarity Score 7 LCS Highest 

Mu mu Highest 

Mu Mu Block MMB Highest 

Mu Mu Sigma Block MMSB Lowest 

Mu Sigma Block MSB Lowest 

Orientation Certainty Level OCL Highest 

Orientation Flow OF Highest 

Power Spectrum PS Highest 

Ridge Valley Uniformity 0 RVU0 Lowest 

Ridge Valley Uniformity 1 RVU1 Lowest 

Sigma sigma Highest 

Sigma Sigma Block SSB Highest 

Table 10: Optimal selection criteria for hda features 

The ROC curves of the best configurations are shown below. Where several 

configurations of a feature are included, they are indicated by the number following 

the feature name. 
Figure 12: Provider 28 ROC curves for hda features 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

49 

Figure 13: Provider 63 ROC curves for hda features Figure 14: ROC curves for hda features for Provider 63 (top line) and provider 83 
The best features are compared in the following ROC curve. Only the best 

configuration per feature is shown. 

Figure 15: Provider 28 ROC curves of best had features 

Figure 16: Provider 63 ROC curves of best features 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

51 

Figure 17: Provider 83 ROC curves of best features 

The following observations were made on the ROC curves: 

 For provider 28 and 63, Local Clarity Score and Orientation Certainty Level gave 

best performance, LCS being slightly better for provider 28. 

 LCS was top performing for configurations 1 and 7. Although the values 

computed with these configurations have a spearman correlation coefficient of 

only 0.57, the scatter plot in Appendix A3 shows that for most fingerprints they 

are very close. 

 For provider 28 and 63, Gabor Shen was clearly at the third rank. 

 For provider 83, LCS and OCL were also top ranked but slightly beaten by Gabor 

Shen for FMR below 0.01%. 

 For provider 28, Mu Mu Sigma Block was on rank four, while for rpoviders 63 nd 

83, the fourth rank was shared by Orientation Flow and Ridge Vally Uniformity. 

 For provider 28, the fifth rank was shared by Mu Mu Block and Ridge Valley 

Uniformity, while for providers 63 and 83, it was taken by Power Spectrum. 

 For all providers, Sigma gave poor performance. 

Overall, the results of the ROC curve evaluation were quite consistent among the 

providers. The top ranking had features are shown in the following Table 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

52 

Provider 28 Provider 63 Provider 83

Local Clarity Score (1&7) Local Clarity Score 

Orientation Certainty 

(1&7)

Level

Local Clarity Score (1&7) 

Orientation Certainty Level

Gabor Shen (low FMR) 

Orientation Certainty Level

Gabor Shen Gabor Shen 

Mu Mu Sigma Block Ridge Valley Uniformity 

Orientation Flow 

0 Ridge Valley Uniformity 

Orientation Flow 

0 

Mu Mu Block 

Ridge Valley Uniformity 0 

Power Spectrum Power Spectrum 

Power Spectrum 

Orientation Flow 

Mu 

Mu 

Mu 

Mu 

Block 

Sigma Block 

Mu 

Mu 

Mu 

Mu 

Block 

Sigma Block 

Table 11: Top ranks of ROC evaluation for hda features 

4.3.1.3 ERC Evaluation 

For the features developed by hda the following ERC curves at 3% FNMR and 10% 

FNMR were obtained. Again, the dotted diagonal line from (0.1, 0) to (0, 0.1) for 10% 

FNMR and from (0.03, 0) to (0, 0.03) for 3% FNMR shows the ideal (and 

hypothetical) ERC in case of a perfect quality filtering, where scores are rejected 

according to their quality, starting with the lowest quality value. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

53 

Figure 18: ERC for hda features at 3% FNMR (top line) and 10% FNMR (bottom line) for provider 28 
It turned out than some features, namely Gabor Segment 9, Mu Mu Sigma Block, Mu 

Sigma Block, Mu and Sigma Sigma Block, resulted in similar ERC where the FNMR 

rapidly decreases for the first 2.5 -3% but from that point on shows only moderate 

reduction. This phenomenon can be explained as follows: approximately 4-5% of the 

genuine comparisons for provider 28 give extremely low comparison scores, which is 

clearly visible in the density function plot below. 

0 2000 4000 6000 8000 10000

0.0000 0.0005 0.0010 0.0015

density.default(x = genuine_scores)

N = 630822 Bandwidth = 135.5

Density

Figure 19: Density function of genuine scores for provider 28 

Approximately 3% of these “bad genuine scores” seem to be caused by fingerprints 

with extreme (highest or lowest) feature values for these features, which implies that 

filtering by these feature values filters very well these 3% of the bad genuine scores. 

At least for Mu Mu Sigma Block, Mu Sigma Block, Mu and Sigma Sigma Block, this 

explanation is supported by the scatter plots of genuine scores (for provider 28) and 

minimum (or maximum, depending on the sign of correlation between feature values 

and genuine scores) quality values of sample and probe fingerprint: for each of these 

features, in the very bottom of the scatter plot there is a strong accumulation or a 

wedge either in the very left (in case where the genuine scores correlate positively 

with feature values) or in the very right (in case of negative correlation). 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

55 

Figure 20: Scatter plot of genuine scores and Mu Mu Sigma Block 

-1 -0.8 -0.6 -0.4 -0.2

0

2000

4000

6000

8000

10000

muSigmaBlock_0

genuine scores

1

752

1502

2253

3003

3754

4504

5255

6006

6756

7507

8257

9008

9758

10509

11259

12010

Counts

 

Figure 21: Scatter plot of genuine scores and Mu Sigma Block 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

56 

Figure 22: Scatter plot of genuine scores and Mu 

Figure 23: Scatter plot of genuine scores and Sigma Sigma Block 

Furthermore, the ERC indicate that the filtering effect by these features is quite 

similar. This hypothesis is supported by the well-separated accumulations in the 

corners of the scatter plots among these features, showing that the subsets of 

genuine scores filtered by these features strongly overlap. This effect is particularly 

strong for Mu Sigma Block and Sigma Sigma Block. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

57 

Figure 24: Scatter plot of Mu Sigma Block and Mu Mu Sigma Block 

Figure 25: Scatter plot of Mu Sigma Block and Mu 

0.2 0.3 0.4 0.5 0.6 0.7 0.8

0.2

0.4

0.6

0.8

1

SSB_1

MSB_0

1

12

23

34

45

56

67

78

89

100

111

122

133

144

155

166

177

Counts

Figure 26: Scatter plot of Mu Sigma Block and Sigma Sigma Block 

Due to the strong initial reduction of the FNMR down to 1% in the ERC for 3% 

FNMR, the features Gabor Segment 9, Mu Mu Sigma Block, Mu Sigma Block, Mu 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

58 

and Sigma Sigma Block resulted in the lowest error rates up to 30% rejection rate. 

For this reason these features were top among the best in the ERC at 3% FNMR 

while only being mediocre performers in the ERC at 10% FNMR and rejection rate 

greater than 15%. This phenomenon resulted in different rankings for 3% FNMR and 

10% FNMR. 

FNMR = 3% FNMR = 10% 

Mu Mu Sigma Block Ridge Valley Uniformity 0 

Gabor Segment (config 9) Orientation Certainty Level 

Mu 

Mu Sigma Block 

Mu Mu Sigma Block 

Sigma Sigma Block 

Gabor Shen 

Orientation Flow 

Local Clarity Score 7 

Ridge Valley Uniformity 0 Power Spectrum 

Orientation Certainty Level Gabor 

Ridge Valley Uniformity 1 Ridge Valley Uniformity 1 

Table 12: Top ranks of ERC evaluation for hda features 

4.4 Overall Comparison 

4.4.1 Correlation 

The following Table shows the spearman correlation coefficients for the hda features 

with the top performing features of NFIQ1 (including Foreground) and FingerJet FX. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

59 

FDA_1 100 -32 24 -35 -6 -36 45 42 69 13 -41 17 81 23 -38 -32 -25 40 7 0 22 24 10 -1 -3 1 1 4

FDA_1

gabor_0GS_5GS_9GSh_10LCS_1LCS_7mu_0MMB_0MMSB_0MSB_0OCL_0OF_2PS_0RVU_0RVU_1sigma_0SSB_1MinQual_7MinQual_9nfiq1_FGnfiq1_NoMinnfiq1_min05nfiq1_QZ3nfiq1_QZ4utility_28utility_63utility_83

gabor_0 -32 100 19 66 89 86 61 -77 -4 13 64 73 -8 66 -38 -37 78 -43 6 -8 15 16 35 -32 21 31 22 24

GS_5 24 19 100 12 20 11 32 -22 14 21 20 27 28 14 -22 -20 16 -18 20 -13 13 17 11 -1 -4 2 2 2

GS_9 -35 66 12 100 47 70 35 -73 0 24 70 37 -27 33 -8 -9 62 -51 39 -35 26 38 17 11 -20 2 0 -1

GSh_10 -6 89 20 47 100 66 72 -57 19 6 37 84 24 76 -55 -52 70 -26 -7 7 16 18 48 -49 39 41 30 34

LCS_1 -36 86 11 70 66 100 57 -79 -12 14 72 63 -23 43 -25 -27 77 -47 28 -39 -4 17 11 -1 -15 14 12 11

LCS_7 45 61 32 35 72 57 100 -41 56 13 23 88 58 70 -64 -57 57 -16 16 -15 28 39 39 -21 8 29 22 25

mu_0 42 -77 -22 -73 -57 -79 -41 100 5 16 -52 -52 24 -37 -6 -7 -93 85 -35 33 -19 -38 -22 -7 11 -2 -1 -2

MMB_0 69 -4 14 0 19 -12 56 5 100 0 -28 36 70 35 -31 -24 12 4 17 -2 38 53 36 -2 0 10 7 11

MMSB_0 13 13 21 24 6 14 13 16 0 100 65 0 -3 13 -42 -47 -27 49 17 -20 12 7 -6 3 -17 1 -2 -4

MSB_0 -41 64 20 70 37 72 23 -52 -28 65 100 24 -43 25 -19 -23 33 -18 32 -36 13 15 1 9 -22 3 0 -2

OCL_0 17 73 27 37 84 63 88 -52 36 0 24 100 49 65 -59 -55 69 -25 4 -7 14 31 46 -36 23 34 27 30

OF_2 81 -8 28 -27 24 -23 58 24 70 -3 -43 49 100 34 -51 -44 -3 25 -8 13 22 27 33 -23 21 19 15 18

PS_0 23 66 14 33 76 43 70 -37 35 13 25 65 34 100 -48 -39 49 -11 -4 14 43 22 46 -40 36 36 24 28

RVU_0 -38 -38 -22 -8 -55 -25 -64 -6 -31 -42 -19 -59 -51 -48 100 86 -7 -35 16 -8 -10 -6 -27 41 -27 -39 -28 -31

RVU_1 -32 -37 -20 -9 -52 -27 -57 -7 -24 -47 -23 -55 -44 -39 86 100 -5 -40 12 -1 3 -7 -22 37 -19 -34 -26 -27

sigma_0 -25 78 16 62 70 77 57 -93 12 -27 33 69 -3 49 -7 -5 100 -76 28 -28 10 36 28 -3 -4 9 8 10

SSB_1 40 -43 -18 -51 -26 -47 -16 85 4 49 -18 -25 25 -11 -35 -40 -76 100 -31 21 -23 -33 -16 -19 11 8 6 5

MinQual_7 7 6 20 39 -7 28 16 -35 17 17 32 4 -8 -4 16 12 28 -31 100 -66 7 53 -9 51 -62 -32 -21 -23

MinQual_9 0 -8 -13 -35 7 -39 -15 33 -2 -20 -36 -7 13 14 -8 -1 -28 21 -66 100 26 -25 39 -55 76 36 22 27

nfiq1_FG 22 15 13 26 16 -4 28 -19 38 12 13 14 22 43 -10 3 10 -23 7 26 100 46 46 -4 24 16 2 9

nfiq1_NoMin 24 16 17 38 18 17 39 -38 53 7 15 31 27 22 -6 -7 36 -33 53 -25 46 100 49 24 -25 -7 -8 -7

nfiq1_min05 10 35 11 17 48 11 39 -22 36 -6 1 46 33 46 -27 -22 28 -16 -9 39 46 49 100 -48 57 33 21 26

nfiq1_QZ3 -1 -32 -1 11 -49 -1 -21 -7 -2 3 9 -36 -23 -40 41 37 -3 -19 51 -55 -4 24 -48 100 -86 -41 -28 -31

nfiq1_QZ4 -3 21 -4 -20 39 -15 8 11 0 -17 -22 23 21 36 -27 -19 -4 11 -62 76 24 -25 57 -86 100 44 29 34

utility_28 1 31 2 2 41 14 29 -2 10 1 3 34 19 36 -39 -34 9 8 -32 36 16 -7 33 -41 44 100 80 83

utility_63 1 22 2 0 30 12 22 -1 7 -2 0 27 15 24 -28 -26 8 6 -21 22 2 -8 21 -28 29 80 100 82

utility_83 4 24 2 -1 34 11 25 -2 11 -4 -2 30 18 28 -31 -27 10 5 -23 27 9 -7 26 -31 34 83 82 100

Table 13: Spearman correlation coefficients of best performing features

An interesting observation is that high correlations (> 0.5) occur among the features 

implemented by hda as well as among FJFX and NFIQ1 features, but not between 

these two groups. This indicates that features from these two groups may well 

complement each other with respect to information content. 

4.4.2 ROC Curve Evaluation 

The following figures show the ROC curves of the top performing features. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

60 

Figure 27: Provider 28 ROC curves of top performing features 

Figure 28: Provider 63 ROC curves of top performing features 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

61 

Figure 29: Provider 83 ROC curves of top performing features 

Following these top performers we found the following features giving next best 

results. 

Figure 30: Provider 28 ROC curves of next best performing features 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

62 

Figure 31: Provider 63 ROC curves of next best performing features 

Figure 32: Provider 83 ROC curves of next best performing features 

4.4.3 ERC evaluation 

The following ERC curves show the best performing features at FNMR of 3% and 

FNMR at 10%. 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

63 

Rejected

FNMR

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30

0.00

0.01

0.02

0.03 NFIQ1_NoOfMinutiae

gaborSegment_9

muMuSigmaBlock_0

muSigmaBlock_0

mu_0

sigmaSigmaBlock_1

Rejected

FNMR

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30

0.00

0.01

0.02

0.03 NFIQ1_Foreground

NFIQ1_QualZone3

NFIQ1_QualZone4

FJFX_MinutiaeQuality_7

orientationCertaintyLevel_0

orientationFlow_2

ridgeValleyUniformity_0

Figure 33: Best features in ERC at 3% FNMR for provider 28 

Rejected

FNMR

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10 NFIQ1_NoOfMinutiae

NFIQ1_QualZone4

FJFX_MinutiaeQuality_7

gaborShen_10

orientationCertaintyLevel_0

ridgeValleyUniformity_0

Rejected

FNMR

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10 NFIQ1_QualZone2

NFIQ1_QualZone3

FJFX_MinutiaeQuality_9

localClarityScore_7

orientationFlow_2

ridgeValleyUniformity_1

sigmaSigmaBlock_1

Figure 34: Best features in ERC at 10% FNMR for provider 28 

4.4.4 Summary 

We obtain the following rankings. 

Correlation ROC (28) ROC (63, 83) FNMR = 3% FNMR = 10% 

NFIQ1 QZ4 LCS1 LCS1 MMSB NFIQ1 QZ4 

GSh LCS7 LCS7 NFIQ NoMin FJFX MinQual7 

NFIQ1 QZ3 OCL OCL GS9 RVU0 

RVU0 GSh GSh Mu OCL 

OCL 

PS 

FJFX MinQual7 

NFIQ1 QZ4 

NFIQ1 QZ4 MSB 

SSB 

NFIQ1 

GSh 

NoMin 

RVU0 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

64 

Correlation ROC (28) ROC (63, 83) FNMR = 3% FNMR = 10% 

RVU1 

FJFX MinQual9 

NFIQ1 QZ3 OF FJFX MinQual7 RVU1 

MMSB PS RVU0 NFIQ1 QZ2 

NFIQ1 QZ1 NFIQ1 NoMin FJFX MinQual7 OCL NFIQ1 QZ3 

NFIQ1 Min05/06 RVU0 MMB NFIQ1 QZ3 FJFX MinQual9 

NFIQ1 Min07 MMB MMSB OF OF 

FJFX MinQual7 

FJFXMinQual10 

OF 

PS 

NFIQ1 QZ3 NFIQ1 

NFIQ1 

QZ4 

FG 

SSB 

NFIQ1 NoMin LCS7 

Table 14: Top ranks of ERC evaluation for all features

From the various evaluation methods, we (subjectively) derive a rough ranking of the 

features (with configuration number indicated) which reflects their priority for 

consideration in the NFIQ2 training process: 

1. NFIQ1 Quality Zone 4 

2. FingerJetFX Minutiae Quality 7 

3. Orientation Certainty Level 0 

4. Ridge Valley Uniformity 0 

5. Gabor Shen 10 

6. NFIQ1 Total # Minutiae 

7. Ridge Valley Uniformity 1 

8. Mu Mu Sigma Block 0 

9. Local Clarity Score 7 

10. NFIQ1 Quality Zone 3 

11. FingerJetFX Minutiae Quality 9 

12. Power Spectrum 0 

13. Sigma Sigma Block 0 

14. Gabor Segement 9 

15. Mu Sigma Block 0 

16. Local Clarity Score 1 

17. FingerJetFX Minutiae Quality 10 

18. FingerJetFX Minutiae Quality 6 

19. Orientation Flow 2 

20. Mu Mu Block 

21. NFIQ1 Foreground 

22. Gabor 0 

23. FingerJetFX Minutiae Quality 8 

24. Freqeuncy Domain Analysis 1 
NFIQ 2.0: Evaluation of Potential Image Features for Quality Assessment 

65 

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