Towards NFIQ II Lite

Self-Organizing Maps for Fingerprint Image Quality

Assessment

NIST Interagency Report 7973

Elham Tabassi, Martin Aastrup Olsen, Anton Makarov, Christoph Busch

Information Access Division

National Institute of Standards and Technology

December 13, 2013
ACKNOWLEDGMENTS

This work was supported by the U.S. Department of Homeland Security Office of Science

and Technology Directorate Interagency Agreement HSHQPM-12-X-00073 Standardization

of Biometric Error Suppression Techniques project and by the German Federal Office for

Information Security.

DISCLAIMER

Hardware and software products identified in this document were used in order to perform

the study described. In no case does identification of any commercial product, trade name,

or vendor, imply recommendation or endorsement by the National Institute of Standards and

Technology, nor does it imply that the products and equipment identified are necessarily the

best available for the purpose.

1
Abstract

Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various

phases in the biometric enrolment and recognition processes. Neglecting quality measurement will

adversely impact the accuracy and efficiency of biometric recognition systems (e.g., verification

and identification of individuals). Measuring and reporting quality allows processing enhancements to increase probability of detection and track accuracy while decreasing probability of false

alarms. Aside from predictive capabilities with respect to the recognition performance, another

important design criteria for a quality assessment algorithm is to meet the low computational complexity requirement of mobile platforms used by military and police forces in national biometric

systems. We propose a computationally efficient means of predicting biometric performance based

on a combination of unsupervised and supervised machine learning techniques. We train a selforganizing map (SOM) to cluster blocks of fingerprint images based on their spatial information

content. The output of the SOM is a high-level representation of the finger image, which forms the

input to a Random Forest trained to learn the relationship between the SOM output and biometric performance. The quantitative evaluation performed demonstrates that our proposed quality

assessment algorithm is a reasonable predictor of performance. The open source code of our algorithm will be posted at http://www.nist.gov/itl/iad/ig/development_nfiq_

2.cfm.
1 Introduction

Recognition performance is increased if fingerprints are of sufficiently good quality and when

overall database integrity is improved. Therefore, the ability to automatically examine a biometric

image and produce a numerical estimate of the utility of the image to a downstream comparison

algorithm (i.e., matcher) is an operationally desirable function. Particularly, if a low quality value

is predictive of recognition failure (primarily a false negative, but possibly a false positive too),

then a new sample can be collected while the subject is still present. Without a method to determine whether the quality of a captured sample is sufficient for recognition purposes, an individual

can be falsely rejected or actively subvert the system. As such, many large scale biometric deployments mandate measuring and reporting of quality scores. Examples are the Unique Identification

Authority of India (UIDAI) [18], the European Union Visa Information System (VIS) [7, 6, 5], or

the United States Visitor and Immigrant Status Indicator Technology (US-VISIT). While the ability to inspect low utility images is essential for both the server and client side of these applications,

the client side demands low computational cost, both in terms of time and processing power. In

fact, requirements for next generation quality assessment includes near frame rate throughput [14].

The major contribution of this paper is a novel method for quality assessment which has a very

low computation cost; essentially, after training the quality assessment is reduced to a look up

table. Our proposed method is based on a combination of unsupervised (self-organizing map [12])

and supervised (Random Forest [2]) machine learning algorithms. A self-organizing map (SOM) is

utilized as a receptive field to learn the spatial information content of a fingerprint image creating

a high level representation in the form of class labels. Transforming an input from image space to

class labels reduces the dimensionality by several orders of magnitude while retaining key characteristics of the finger image. This new representation of the finger image becomes input for the

Random Forest which is to give a quality score that is predictive of biometric performance. To the

authors’ knowledge this is the first application of using a receptive field approach to fingerprint

quality assessment.

The paper is organized as follows: Section 2 provides a quick overview of related work in

fingerprint quality assessment. Section 3 details our proposed method. Discussion of data, experiments and results are given in Section 4, followed by conclusions and future work in Section 5.

2 Background and Related Work

Several fingerprint quality assessment algorithms have been documented in the literature, a comparative study of them is given in [1]. The majority of them are based on processing an image in either the spatial or frequency domain to compute particular features such as orientation

strength. Subsequently a scalar quality value is computed from a feature or a combination of features. The technical report on fingerprint sample quality [11] by the International Organization for

Standardization has a recent overview of recommended fingerprint quality features; e.g., Orientation Certainty Level which measures the strength of the energy concentration along the dominant

ridge flow orientation within a block by computing the blockwise gradient; Ridge-Valley Structure which computes the blockwise clarity of ridges and valleys by applying linear regression to

determine a gray-level threshold and then classify pixels as ridge or valley. A ratio of misclassified pixels is determined by comparing the normalized ridge and valley thickness of that block.

Orientation Flow proposed in [3] is a measure of the rate of change in the blockwise ridge flow

across the fingerprint. The quality score decreases as the difference between the dominant ridge

orientation of the block and its 8 neighboring blocks increases. A block wise Gabor-based quality

1
feature was proposed in [16] and a point wise Gabor based quality feature was proposed in [15].

One widely used open source quality assessment algorithm which combines several of the above

features is NFIQ [17]. It is based on a neural network trained on an 11-dimensional feature vector

derived from finger image characteristics which include minutia count, ridge curvature and local

contrast.

Computation of these features happens to be expensive, first a processing unit is required for

their computations, and secondly according to our in-house implementations, it takes more than

130 milliseconds to compute any of the features. In this paper, we aim to address this shortcoming

of the current methods. Once trained, our proposed method is essentially reduced to look-up table

operations, which reduces the computation time by about half, and make it suitable for mobile

platforms with minimum computational resources.

3 Methodology

This section details how we employed the SOM and Random Forest clustering for calculation of

fingerprint image quality. Fig. 1 shows the overview of our proposed method. We give a review

of the SOM concept in Section 3.1, and its application to our proposed method in Section 3.2.

Section 3.3 describes how we used Random Forest to classify the output of the SOM into five

different quality categories.

Figure 1: Overview of system architecture and components. The training and validation sets and their

binned associated comparison scores are utilized to train the self-organizing map and the Random Forest.

Training the SOM creates a codebook which is used to create an activation histogram of the training and

validation sets. The histogram of activations from the training set is used as the explanatory variables in the

Random Forest, while the binned comparison score is the target variable. Once trained, the Random Forest

produces a model which is verified using the validation set. The test set, containing the unseen data, is used

to determine the accuracy of the pipeline.

3.1 Overview of Self-organizing Maps

The Kohonen Map, introduced in [12], is a self-organizing map (SOM) which is trained without

supervision. Its objective is to organize the input patterns to a topological structure where the

relations between different patterns are preserved. The SOM is a two-layered network consisting

of a number of processing units. The first layer of units can be considered as a group of sensors

picking up the signal. It is fully connected to a second, two-dimensional layer: the competitive

layer. Fig. 2 shows the topology of the network. The weights associated with the connections are

adjusted during training and only one single unit in the competitive layer is active at a time. Due

to the training rules explained below, the spatial distance of two units reacting on different input

patterns is a measure for the similarity of the two patterns. The training of the network is carried

out by presenting data vectors x to the input layer of the network whose connection weight vectors

mi of all competitive units i are first initialized by random values. If N is the dimension of the

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data, we choose N input units of the first layer and define an Euclidean distance di between x and

mi with

di = k x − mik =

vuut

N

X−1

j=0

(xj − mij )

2 (1)

The input signal activates winner unit c whose weight vector has the minimum distance to the

given signal. The winning unit is also known as the best matching unit (BMU).

dc = min(di) (2)

The updating of the weights mij associated to the units is only performed for units i within a

proximity d(t) defining the time-dependent neighborhood Nc(t) of the winning c. This proximity

d(t) is reduced as training of the network is progressing with time t (Eq. (6)). Fig. 2 illustrates

the time-dependent neighborhood. The update of weights follows Eq. (3), where a(t) represents a

time-dependent learning rate.

m

(t+1)

ij = m

(t)

ij + ∆m

(t)

ij (3)

∆m

(t)

ij =

(

a(t)(xj − m

(t)

ij ), if unit i ∈ Nc(t)

0, otherwise

(4)

a(t) = a0(1 −

1

T

), t ∈ [0, . . . , T − 1] (5)

d(t) = d0(1 −

t

T

), t ∈ [0, . . . , T − 1] (6)

Figure 2: Topology of the self-organizing map and the time dependent neighborhood Nc(t) of a

unit c.

The SOM offers two properties during training that are strongly related to our challenge namely

a) an approximation of the presented data by mean vectors mithat are associated as weights to

the units thus essentially realizing a cluster analysis of the data and b) a topological ordering of

the competitive units in such a way that neighboring units in the layer represent similar clusters in

multidimensional space and thus a dimensionality reduction. This can be interpreted as a nonlinear,

topology preserving, associative mapping process. Generally, the Kohonen Map refers to the wellknown K-means clustering [4]. As the units in the competitive layer are ordered topologically, i.e.,

neighboring units react to data vectors similar in the input space, the mapping can be interpreted as

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a reduction of any N-dimensional space into two dimensions preserving the topology of the data as

much as possible. The resolution of this discrete 2D-space is given by the number of competitive

units, i.e., clusters, which should be configured such that the map serves the requirements with

regard to grading (granularity) of the quality score. The limited use case of the SOM for clustering,

topological organization and for subspace mapping has been described. Due to the fixed number

of units in the SOM and its topology preserving property, some units may have very similar mean

vectors mi. With a trained SOM we then assign a meaning or class to each unit, such that its

activation can be related to biometric recognition performance. Our SOM input is blocks of the

fingerprint image, and therefore we do not know the contribution of each individual block towards

the resulting comparison score. Furthermore, comparison scores are pairwise while quality scores

only relate to a single impression. Thus we cannot know if a low comparison score is due to both

impressions being of low quality or only one of them. We uncover the relation between each unit

in the SOM and biometric performance through another layer of machine learning. In particular we

utilize Random Forests to determine the relation between a certain activation pattern and a given

comparison score (see Section 3.3). During the organization process the goal is to find a limited

set of centroids representing the data in a C-means sense. As a result the centroids represent the

variety of feature vectors in an optimal way leading to a minimum of the error function, which

can be easily calculated by the Euclidean distance between all feature vectors and their nearest

codebook vector in the map. This search does not need to cover the entire space of codebook

vectors since efficient approximations can exclude the vast majority of codebook vectors from

consideration [19]. The unit which is closest to the input vector is known as the best matching

unit (BMU).

3.2 Application of Self-organizing Maps

First, to provide a fixed length input vector for the SOM the finger images are subdivided without

padding or overlap into blocks of size n ×n pixels after intensity normalization. One-dimensional

vectors of length n

2

are constructed from the pixel intensities in each block by concatenating the

rows. For the training a SOM of size dim × dim is initialized with randomly generated weight

vectors. During the training phase each training input vector (i.e., the pixel intensities of a block)

is presented to the network and modification of network weights is performed. After T iterations

(assuming T is large enough) the SOM clusters the training input vectors into dim × dim units

representing different spatial characteristics of fingerprints. The trained SOM produces a codebook

with dim × dim entries each of length n

2 where each codebook entry represents a cluster of input

training vectors.

Due to the update function (Eq. (3)) the codebook vector (or entry) associated with each unit

will stabilize such that nearby units will have similar codebook vectors. Figure 3 shows the codebook vectors of a SOM of size 24 by 24 (dim = 24) trained using input vectors of length 576

(n = 24) for T = 1 × 106iterations. It is clear that the SOM has identified clusters representing various ridge orientations, ridge strengths and background. Variation in image intensity in

the codebook is absent or limited due to the image normalization performed as explained in Section 4.1. The trained SOM assigns an index of the corresponding best-matching unit to each block

of an input image. Fig. 4 shows the unit activation patterns for a finger image. Note that the

white background appearing in the input images shown in Fig. 4(a) activates the same units in

the SOM (shown in Fig. 4(b)) indicating that the background has a cluster of its own. To summarize, a trained SOM transforms a fingerprint image from its spatial domain into a vector of the

SOM unit activations. The length of this vector varies for different images because the number of

blocks depend on the image size. To get a fixed-length representation we compute the histogram

of unit activations normalized by the block count per image. Each image block in a finger image

4
has exactly one BMU and thus we can form a histogram of unit activations. This is motivated by

the expectation that the activation patterns for high quality and low quality images are different.

Therefore, an image of size U × V is represented by a vector of length dim × dim with each

component being the frequency of occurrence of the codebook entries.

Figure 3: Visualization of the codebook for a SOM of size 24 by 24 (dim = 24) trained with 1.7×107input

vectors of length 576 (n = 24) for T = 1 × 106iterations shown in a grid structure. Unit 0 is located in

upper left corner and unit 575 is located in the lower right corner.

(a) (b)

Figure 4: Illustration of relationship between finger image and SOM codebook shown in Fig. 3. 4(a) is the

input images which is divided into blocks of size 24×24. 4(b) shows the best-matching unit for each image

block where black is unit 0 and white is unit 575. The image is FVC 2004’s image id 102 [13].

To determine how the SOM behaves when presented with low quality (i.e., difficult to match)

and high quality (i.e., easy to match) fingerprint images we select 64 images that result in false nonmatches at the threshold corresponding to false match rate of 1×10−4

(this set is denoted Low)

and 64 images involved in comparisons giving the highest possible comparison score (this set is

denoted High). Note that the threshold was computed over 120 000 impostor comparisons. Fig. 5

5
shows 10 randomly chosen examples of activation histograms from each of the Low and High sets.

The SOM is of size 32 × 32 (dim = 32) and the network was trained for T = 1 × 106iterations

using image blocks of 24 × 24 pixels (n = 24). Visual inspection reveals that the histograms

of images in the low and high sets look different. Fig. 6 shows the sum of SOM unit activations

of the 64 images in the High and Low sets respectively. The difference of these two aggregated

histogram (Low − High) is depicted in Fig. 7. Bars that extend below zero represent units with

stronger activation in the High set than the Low. Similarly bars that extend above zero represents

units with stronger activation in the Low set. Since the difference between the two sets is nonzero we conclude that the histograms of the two sets are substantially different. In other words,

we have shown that the activation histogram of images involved in a false reject are significantly

different from that of those which are highly likely to be correctly recognized. Therefore we

used this activation histogram as our feature vector for a supervised learning to predict biometric

performance.

3.3 Classification using Random Forest

We chose bins of genuine comparison scores as the response variable and tried several classifiers:

K-Nearest Neighbors, Support Vector Machine, Recursive Partitioning tree, and Random Forest.

We only present results of Random Forests which performed better than the others. Random

Forests [2] basically grows many classification trees. To classify a new object from an input

vector, it presents the input vector to each of the trees in the forest. Each tree gives a classification,

in other words the tree votes for that class. The forest chooses the classification having the most

votes. Random Forest has the advantages of being fast, capable of handling large datasets, and that

it does not over fit. We investigate network dependence on block size, network size and number of

iterations by varying n and dim.

Table 1 summarizes the accuracy of the Random Forest for the different parameters. It shows

that using smaller block size gives better performance. Increasing the size of the network does

not seem to have a significant effect, which might be due to lack of sufficient training data. Theoretically, the size of the map is only limited by the memory capacity of the computing unit and

availability of sufficient number of training images. A larger SOM network is more capable of

representing all possible spatial information of its training data, but it requires a larger number of

images for training.

dim = 24 dim = 32 dim = 40

n = 24 26.38 % 26.76 % 26.23 %

n = 32 25.85 % 24.32 % 26.34 %

n = 64 23.18 % 22.73 % 24.18 %

Table 1: Accuracy of prediction of comparison score bin for various parameter choices for the

SOM dimensions (dim) and block sizes (n). The percentage reflects the accuracy of the predicted

bin matching the actual bin as determined from the comparison score.

4 Experiments and Results

4.1 Data set

Data we used comprised of 7976 right index finger images of 3988 subjects (2 images per subject)

selected from a large operational repository. We took care to construct a balanced training set

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Figure 5: On the left, histogram of activations for 10 images from the set of images which occurred

in comparisons resulting in false non-matches at FMR = 1×10−4(set High). On the right, histogram of

activations for 10 images from the set of images which occurred in comparisons with the highest possible

comparison score (set Low). The numbers on the right side of each panel (shaded in gray) indicate the

image anonymized identification key.

7
0.0

0.2

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High Low

0 128 256 384 512 640 768 896 1024

SOM Unit

Figure 6: Aggregated histograms of the Low and High sets. The upper figure shows the aggregate histogram of 64 images that gave the highest possible comparison score (easy to match and therefore high

quality). The lower figure shows the aggregate histogram of 64 images that produces false non-match at

threshold corresponding to false match rate of 1×10−4.

−0.25

0.00

0.25

0.50

Difference

0 128 256 384 512 640 768 896 1024

SOM Unit

Figure 7: Difference between aggregated histograms of the Low and High sets. Negative values indicate

that the unit is more active in images that gave the highest possible comparison score (easy to match and

therefore high quality), while positive values indicate that the unit is more active in images that gave a false

reject (therefore hard to match or low quality).

(1000 pairs) with a representation of real world difficult to match samples to the extent possible.

Comparison scores are generated using one of the leading commercial comparison algorithms.We

want the training data to be a balanced representation of all possible (or to the extent possible)

fingerprint impairments, as well as pristine images. We found that the current available public

datasets were not suitable for our work. This is mainly due to small size of these datasets (a few

hundred users at the most) and also absence of a reasonably wide range of quality problems in

these collections, which is expected and understandable given the careful and at times controlled

design of these collections. To ensure inclusion of a wide range of quality in our dataset, we

formed a balanced representation from all possible comparison score values generated by the biometric comparator used in our study. Given that different levels of fingerprint quality result in

different values of comparison scores, a set of images representing all possible comparison scores,

would represent the obtainable diversity in the quality. Specifically, we chose images from a large

operational repository using comparison scores of a leading commercial fingerprint comparison

provider. First, we quantized the raw comparison scores by dividing their range into 100 bins.

Then for each bin of comparison scores, we randomly selected 10 pairs of images with the corresponding quantized comparison score. This gave 2000 fingerprint images (10 × 100 comparisons,

2 images per comparison) from which we used 1800 for training and 200 for validation. Our

test set consists of images from 2988 comparisons (5976 fingerprint images) selected at random

from the initial corpus excluding images occurring in any of the 1000 comparisons selected for

the training or validation sets. We further grouped the quantized comparison scores (which are in

[1-100]) into 5 levels such that quantized scores in [1, 20] belongs to bin 1, [21, 40] belongs to

8
bin 2 and so on. These 5 levels quantify the biometric performance for each pair of images and

are used as the target (response) values for a Random Forest whose explanatory variables are the

SOM activation histogram. Images are normalized to give equal intensity according to the pyramid

based enhancement method from [8]. An example of the enhancement process is shown in Fig. 8.

(a) (b)

Figure 8: Example of normalization process using image from FVC 2004 [13]. Image (a) is the input

image and (b) is the enhanced version.

4.2 Performance measurements

This section documents methods and results for the quantitative evaluation of our proposed quality assessment approach. Evaluations are done by quantifying the association between quality

scores and the observed recognition performance. We used three widely accepted and documented

methods to assess the goodness of our proposed quality algorithm in predicting performance. We

examined the relationship of quality scores with a) genuine comparison scores, b) recognition error rate (FNMR) for each level of quality, and c) improvement of FNMR as poor quality samples

are removed. Per the definition of biometric quality [10], quality values q should be a monotonic increasing function of performance, where high-quality samples give high similarity scores

when involved in genuine comparisons. Figure 9 shows the relationship of our proposed quality

scores with biometric comparison scores, we generated heatmaps (i.e., surface plots) of the three

variables qverify , qenrol and score of the images in our test sets. The x-axis represents quality of

the enrolment samples. Quality of verification samples are on the y-axis. The color in each cell

represents the magnitude of genuine comparison (or similarity) scores. If quality is an indicator

of performance, higher similarity scores (shown in light gray) will be achieved for high-quality

images of the same finger. The heatmap in the lower right corner shows the magnitude of samples

in each of the combinations of score for qverify , qenrol . Of the block sizes investigated, block

size 24 gives the best performance; images with higher quality score (4 or 5) result in the highest

similarity scores (visualized by light gray values in the upper right corner) and images with low

quality scores (1 or 2) result in the lowest similarity score (visualized by black color) in the lower

left corner. Block size 64 does not perform well.

A quality assessment algorithm is useful if it can at least give an ordered indication of an

eventual performance. This is the case for our proposed method as shown in Figure 10. Figure

10 shows the FNMR for each L = 5 levels of our proposed quality method. The highest FNMR

is observed for the lowest quality samples (q = 1), and lowest FNMR is observed for the highest

quality samples (q = 5). Biometric comparison involves at least two samples and the challenge is

then to relate performance (which involves two samples) to quality values qverify and qenrol . We

simplify the analysis by combining the two qualities according to qi = M(qverify , qenrol). It is

usually the case that operationally a quality assessment algorithm can be used to assess whether

9
an enrolment sample is of high quality. The enrolment sample will be compared later with a

sample that typically is of less controlled quality. We used M(x, y) = min(x, y) which captures

the concept that the worse of two samples drives the similarity score. Figure 10 shows that our

proposed method provides a rank order indication of FNMR: the highest error rate is observed for

the lowest quality sample (q = 1) and the lowest error rate for the highest quality (q = 5). As

such, our proposed method is successful in predicting performance.

Enrolment Quality Blocksize 24

Verification Quality

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Figure 9: Genuine comparison scores vs (qverif , qenrol) for the 3 different block sizes. The proper

behavior is to have low-quality scores for low genuine scores, as is the case for block sizes 24 and 32. The

figure in bottom right shows the count of images involved in each cell.

Another metric for comparative evaluation of quality assessment algorithms is the error versus

reject curves [9]. The goal is to demonstrate how efficiently rejection of low-quality samples

results in improved performance. This models the operational case in which quality is maintained

by reacquisition after a low-quality sample is detected. Consider that a pair of samples (from

the same subject) with qualities qverif and qenrol are compared to produce a genuine score, and

this is repeated for N such pairs. If the quality values are perfectly correlated with the genuine

comparison scores, setting threshold τ to give an overall FNMR of x and then rejecting x percent

with the lowest qualities should result in FNMR of zero after recomputing FNMR. Figure 11 shows

the error vs. reject curves of our proposed method as applied to different block sizes, namely

24, 32, and 64. We set the value of τ to give a false non-match rate of 10 %. Pairwise quality

is computed using the geometric mean of the quality scores of the two images being compared.

Similar results were obtained using min(x, y). Behavior of a perfect quality assessment algorithm

is displayed by the gray dotted line where the rejection of the lowest 10 % quality would result

in an FNMR of zero. The most operationally relevant part of the error vs. reject curves is usually

on the left side where a small fraction, x, of low-quality rejections would be tolerable from the

perspective of forcing a second enrolment attempt. We set x = 0.3, because rejection of more

than 30 % is not operationally feasible. SOM block size of 24 performs the best as it is evident

by the sharpest decrease in FNMR when poor quality samples are rejected. Rejection of 10%

10
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Figure 10: FNMR vs. score threshold for each level of quality for different block sizes. The SOM trained

on block sizes 24 and 32 performs better than block size 64, as is indicated by a better separation of FNMR

for the five levels of quality.

0.00 0.05 0.10 0.15 0.20 0.25 0.30

0.070 0.080 0.090 0.100

fraction of genuine comparisons rejected

false non−match rate

networkSize 24

networkSize 32

networkSize 64

orientationCertaintyLevel

Figure 11: FNMR vs reject curves for our proposed method. The threshold is set to give an initial FNMR =

0.1. The gray dotted line shows the ideal case where the rejection of the ten percent lowest quality results

in zero FNMR. Blocksize = 24 perform the best as it is evident by the sharpest decrease in FNMR as poor

quality samples are rejected. An almost flat curve suggests that the quality algorithm is not effective in

predicting the performance. The solid gray line shows the rejection curve for Orientation Certainty Level

metric. Our proposed method outperforms Orientation Certainty Level for rejection rates higher than 5 %.

lowest quality decreases FNMR to 0.085 from its initial value of 0.1. To give a point of reference

for benchmarking our method and existing methods, Fig. 11 includes an existing known quality

assessment method - the Orientation Certainty Level [11]. Our proposed method outperforms

Orientation Certainty Level for rejection rates higher than 5 %.

Computation efficiency: We noted the processing time required to compute quality values

on a PC equipped with a 2.3 GHz Intel Core i7 and 16 GB of memory. With a network size of

dim = 24 the quickest processing time was recorded at a block size of n = 24 with 503 seconds

to generate quality values for 7973 images ( 65 ms per image). With the same network size but

block size n = 64 we recorded a total processing time of 654 seconds ( 82 ms per image).

We conclude this section by noting that all three evaluation methods employed here demonstrated that our proposed quality algorithm is capable of predicting performance of a commercial

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biometric comparison subsystem.

5 Conclusions and Future Work

We presented a novel approach for fingerprint quality assessment which is fast and of low computational complexity. Our approach is predictive of biometric performance and avoids the explicit

quality feature extraction that often has high computational cost. This is achieved by training a self

organizing map to represent the variety of spatial information content of fingerprints in our training

data. The training of the SOM network may require a significant computational effort, however the

training is performed only once, in an offline setting, using suitable computing resources. Once

the code book is generated, the operational mode is extremely efficient as the implicit quality

assessment is reduced to a loading-task of the fingerprint-image blocks to the input layer of the

trained SOM, and a subsequent lookup of the quality level in the associated Random Forest. Thus

our suggested approach has the advantage of having a very low computational cost which makes it

particularly suitable for applications constrained in processing resources such as low-end mobile

fingerprint devices used by the Unique Identification Authority of India (UIDAI). We investigated

the effect of finger image block size (input to the SOM), and the size of the SOM network. Our

experimental results suggest that smaller image block size (24 pixels by 24 pixels in our case)

performed better than larger sizes (32 or 64 pixels). A larger SOM network while more capable

of representing all possible spatial information of its training data, requires a larger number of

images for training. We tried network sizes of 24 (576 units), 32 (1024 units), and 40 (1600 units).

A network size of 32 outperformed the others, noting that our training data might not have been

large enough for network size of 40. Quantitative results presented in Section 4.2 demonstrate the

predictive power of our proposed finger quality assessment method.

Future work will address improvements in the SOM training, presentation of SOM output to

the learning algorithm (Random Forest), and its fine tuning. A potentially valuable source of

information is the distance between an image block and its associated BMU. This information

could be used to create a weighted SOM activation histogram. Combining units with low distance

between weight vectors may provide more robust classification. Additionally, we plan to repeat

the study using comparison scores of one or more comparison subsystem (or algorithms) and

investigate its generalizability to comparison algorithms not used at the training time. Finally, we

are eager to try this approach for the quality assessment of latent impressions.

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