Fingerprint Classiﬁcation F - NTU · Au1 Fingerprint Classiﬁcation ... shows a stable and reliable feature and is employed in ... struggle against the large intra-class and small

Encyclopedia of BiometricsComp. by: ASaid Maraikayar Stage: Galleys ChapterID: 0000883564 Date:23/1/09 Time:20:43:57

F

FingerprintAu1 Classification

XUDONG JIANG

Nanyang Technological University, Nanyang Link,

Singapore

Synonyms

Fingerprint indexing; Fingerprint pre-matching; Finger-

print retrieval

Definition

Fingerprint classification is a procedure in which fin-

gerprints are grouped in a consistent and reliable way,

such that different impressions of a same finger fall

into a same group. It can be viewed as a coarse-level

pre-matching procedure so that a query fingerprint

needs to be further compared with only a smaller

subset of fingerprints in the database belonging to the

same group. It is often necessary to integrate a classifi-

cation module into a fingerprint identification system

to speed up the database search. A database can be

partitioned into ▶ human-interpretable fingerprint

classes based on Galton–Henry scheme or into

▶machine-generated fingerprint classes.

Introduction

A fingerprint recognition system captures a user’s fin-

gerprint and compares it with the information stored in

a database to establish or to authenticate his/her identi-

ty. If an identity is claimed, the system compares the

query fingerprint only with the template corresponding

to this identity stored in the database. This one-to-one

matching process is called fingerprint verification. If no

identity is claimed, the system needs to compare the

query fingerprint with all templates stored in the data-

base to establish the identity. This one-to-many match-

ing process is called fingerprint identification. The

extension of the one-to-one matching of a verification

system to the one-to-many matching of an identifica-

tion system increases the possibility of false positive

matching. Comparing to the verification performance,

both accuracy and speed may deteriorate significantly if

a verification algorithm is naively extended to solve an

identification problem. The performance deterioration

could be very serious for large-scale identification sys-

tems as it is directly proportional to the number of

fingerprints in the database [1]. This problem can be

alleviated by reducing the search space of exact match-

ing. Fingerprint classification, indexing, or retrieval

techniques facilitate the reduction of the search space.

They can be viewed as a coarse-level pre-matching

process before further exact matching in an identifica-

tion system. A query fingerprint is first compared to

prototypes of the pre-specified classes, bins or clusters

to find its class membership. Then, it is only necessary

to compare the query fingerprint exactly with a subset

of the database that has the same class membership.

For example, if a database is partitioned into ten

groups, and a query fingerprint is matched to two of

the ten prototypes, then the identification system only

needs to search two of the ten groups of the database

for exact matching. This reduces the search space by

fivefold if fingerprints are uniformly distributed in the

ten groups.

The first rigorous scientific study on fingerprint clas-

sification was made by Sir Francis Galton in the late

1880s [2]. Classification was introduced as a means of

indexing fingerprints to speed up the search in a data-

base. Ten years late, Edward Henry refined

Galton’s work and introduced the concept of finger-

print ‘‘core’’ and ‘‘delta’’ points for fingerprint classifi-

cation [3]. Figure 1 shows the five most common

# Springer-Verlag Berlin Heidelberg 2009

Fingerprint Classification

439

Comp. by: ASaid Maraikayar Stage: Galleys ChapterID: 0000883564 Date:23/1/09 Time:20:43:58

classes of the Galton–Henry classification schemewhere‘

the core and delta points and the class names are shown.

Henry’s classification scheme constitutes the basis for

most modern classification schemes. Most law enforce-

ment agencies worldwide currently employ some var-

iants of this Galton–Henry classification scheme.

Although Galton–Henry scheme has some advantages,

such as human-interpretable and rigid segmentation of a

database, only a limited number of classes are applicable

to the automated system. For example, most automated

systems [4–8] can only classify fingerprints into five

classes as shown in Fig. 1. Moreover, fingerprints are

not evenly distributed in these classes and there are

some ambiguous fingerprints that cannot be reliably

classified even by human experts. Therefore, Galton–

Henry scheme that partitions the database into

human-interpretable fingerprint classes is not immune

to errors and does not offer much selectivity for fin-

gerprint searching in large databases.

In fact, it is not obligatory for an automated system

to partition the database into human-interpretable

fingerprint classes. In automatic fingerprint identifi-

cation systems (AFIS), the objective of the classification

is to reduce the search space. This objective can be

accomplished by partitioning the database into ma-

chine-generated fingerprint classes in feature space as

long as the classification is consistent and reliable.

For example, some fingerprint index techniques

[9, 10] can reduce the search space more efficiently

than the Galton–Henry scheme. Continuous classifica-

tion techniques [1, 11, 12] do not pre-classify the

database, but represent each fingerprint with a numer-

ical feature vector. Given a query fingerprint, a class is

formed by retrieving a portion of fingerprints from

database whose feature vectors are close to that of the

query fingerprint. Although these techniques can clas-

sify fingerprints into large number of classes, a query

fingerprint needs to be compared with all fingerprints

in the database, which could be time consuming for

a large database. This problem can be circumvented

by incorporating data clustering techniques in the

▶fingerprint retrieval framework [12, 13].

Fingerprint Classification. Figure 1 Six sample fingerprints from the five commonly used fingerprint classes

(arch, tented arch, left loop, right loop, and whorl) under the Galton–Henry classification scheme where two

whorl fingerprints are shown (a plain whorl and a twin loop whorl). Singular points of the fingerprints, called core and

delta, are marked as filled circles and triangles, respectively. Note that fingerprints of an arch class have neither core

nor delta.

2 F Fingerprint Classification

440


Feature Extraction for Classification

Not all measurements of a fingerprint image remain

invariant for a given individual over the time of cap-

ture and can be used to discriminate between identi-

ties. The first step of fingerprint classification is to find

salient features that have low intra-class variation and

high inter-class variation. Fingerprint image is an ori-

ented texture pattern that contains ridges separated by

valleys and exhibits two levels of feature as shown in

Fig. 2. At the global level, the orientation field and the

ridge frequency are two primitive and fundamental

features. At the fine local level, the most prominent

characteristics are the minutia points, where a ridge

terminates or separates into two ridges.

An orientation field shown in Fig. 2b of a finger-

print shown in Fig. 2a contains information about the

local dominant orientations of fingerprint ridges, from

which some other features can be derived such as

singular points and dominant ridge line flow as

shown in Fig. 2. The dominant ridge flow is repre-

sented by a set of curves running parallel to the ridges

lines but not necessarily coinciding with ridges and

valleys. There are two types of singular points: core

and delta points. A core point is the turning point of an

inner-most ridge and a delta point is a place where two

ridges running side by side diverge. Orientation field,

dominant ridge flow, and singular points are useful

features for classification. A local ridge frequency is

the number of ridges per unit length along a hypothet-

ical segment orthogonal to the local ridge orienta-

tion. Its’ inverse is the local ridge distance as shown

in Fig. 2a. Although the local ridge distance varies

across different fingers, it is difficult to serve as a

reliable feature due to its high within-finger variation

caused by the discontinuity of ridges and valleys and

various unfavorable skin and imaging conditions.

However, the average ridge distance over a fingerprint

shows a stable and reliable feature and is employed in

some approaches [12, 13].

Minutia points as shown in Fig. 2a are in general

stable and robust to fingerprint impression conditions.

They often serve as discriminative features for exact

matching in most automatic fingerprint recognition

systems. However, some fingerprint indexing appro-

aches [9, 10] also use minutiae for coarse level finger-

print search. Another type of feature is the filter

response of fingerprint image. Gabor filters are orient-

ed band-pass filter with adjustable frequency, orienta-

tion, and bandwidth parameters. The responses of

Fingerprint Classification. Figure 2 A fingerprint image and its feature representation. The orientation field consisting

of fingerprint local orientations is represented by short lines in (b). Core and delta points are marked in both (a) and (b) by

filled circles and triangles, respectively. Two examples of ridge ending and ridge bifurcation, called minutia points, are

enclosed by circles and squares in (a), respectively. An example of local ridge distance is shown by two arrows in (a). Three

dominant ridge flow curves that can represent the Galton–Henry classes (here: right loop) are shown in both (a) and (b).

Fingerprint Classification F 3

441


Gabor filters capture information of fingerprint local

orientation, ridge frequency, and ridge discontinuity

and hence can be used for both coarse level classifica-

tion [5] and exact matching.

Classification Under Galton–HenryScheme

Over the last four decades, many techniques have been

developed for the automatic classification of finger-

prints under Galton–Henry scheme, which can be

coarsely assigned to one of these categories: rule-

based, syntactic, structural, statistical and other

approaches. While rule-based, syntactic and structural

approaches are mainly used to partition the database

into the human-interpretable fingerprint classes de-

fined by Galton–Henry Scheme, statistical approaches

are able to classify fingerprints into compact clusters in

feature space.

The rule-based approaches codify the human

expert knowledge of manual classification such as the

singularity and the geometrical shape of ridge lines. It

is not difficult to see from Figs.1 and 2 that the five

human-interpretable fingerprint classes can be deter-

mined by the number and location of the singular

points plus some local ridge orientations. Fingerprints

with neither core nor delta points are classified as arch.

Whorls (plain whorl and twin loop whorl) have one

or two cores and two deltas. Loops and tented arch

contain only one core and one delta. Tented arch is

discriminated from loops by examining the local

orientations lying along the line connecting the core

and delta points. The difference between these local

orientations and the slope of the line is much smaller

for a tented arch than loops. Left and right loops are

distinguished by examining the local orientations

around the core point with respect to the slope of the

line [6]. Although a rule-based approach is simple and

work well on rolled fingerprint with high image quali-

ty, robust and consistent detection of singular points in

a poor quality fingerprint remains a difficult task.

Thus, the rule-based approaches are in general sensi-

tive to noise and cannot work on the partial fingerprint

where the delta point is often missing.

A syntactic method represents a fingerprint by a

sentence of a language extracted from the ridge flow or

orientation field. For example, the three dominant

ridge flow curves in Fig. 2 show the typical pattern of

right loop. It is not difficult to see from Figs.1 and 2

that, in general, the five human-interpretable finger-

print classes can be distinguished by such dominant

ridge flow curves. In the syntactic approaches, a gram-

mar is defined for each fingerprint class to build up

sentences. Classification is performed by determining

which grammar most likely generates the sentence

extracted from a query fingerprint. In general, syntac-

tic methods tend to be robust in the presence of image

noise but often require very complex grammars to

struggle against the large intra-class and small inter-

class variations. Complex grammars often result in

unstable classification.

The structural approaches organize low-level

features into higher-level structure. One approach

partitions the orientation field into connected regions

characterized by homogeneous local orientations [11].

For example, it is not difficult to identify some homo-

geneous orientation regions from the orientation field

shown in Fig. 2b. A relational graph that shows the

relations among these regions of a fingerprint contains

discriminative information for classification. An inex-

act graph matching technique is exploited to compare

the relational graphs with class-prototypes. As a robust

and consistent partition of orientation field is not an

easy task, a template-based matching is developed

to guide the partitioning [11]. Another approach

converts the two-dimensional fingerprint structure

into one-dimensional sequence and exploits hidden

Markov model for classification [8]. A set of horizontal

lines across the fingerprint is used to extract a sequence

of features. It captures information about the local

orientations and ridge distances and thus has higher

discrimination power than the orientation field alone.

Since the structural approaches rely on global structur-

al information, they can work on noisy images and are

able to deal with partial fingerprints where some sin-

gular points are not available.

Statistical approaches extract a fix-size numerical

feature vector from a fingerprint and exploit statistical

classifiers, such as k-nearest neighbor classifiers, sup-

port vector machines and artificial neural networks.

The feature vector can be constructed based on the

orientation field [4, 11, 12] or the responses of Gabor

filters [5]. As features extracted from different finger-

print regions show different discriminating power,

some weighting schemes [4, 11, 12] or non-uniform

spacing techniques [5, 13] are developed to put higher

weights in more discriminative regions of fingerprint.


442


Karhunen–Loeve (KL) transform and multi-space KL

(MKL) transform [14] are also applied to reduce the

dimensionality of feature vector. Statistical classifiers

in general need to be trained with a fingerprint data-

base. As Galton–Henry scheme defines the human-

interpretable fingerprint classes rather than the natural

clusters of fingerprints in feature space, supervised

training using fingerprint samples with known class

labels is often applied. On the other hand, statistical

approaches are able to classify fingerprints far beyond

the Galton–Henry scheme into much more classes.

Classification with Machine-Generated Classes

The Galton–Henry Scheme does not offer much selec-

tivity for fingerprint searching in large databases. Most

automated systems [4–8] can only classify fingerprints

into the five classes shown in Fig. 1 and the probabil-

ities of the five classes are approximately 0.037, 0.029,

0.338, 0.317, and 0.279 for the arch, tented arch,

left loop, right loop, and whorl, respectively [15].

The uneven distribution of these human-interpretable

fingerprint classes further lowers the classification

efficiency. In fact, for the application of the automated

identification, it is often not obligatory to partition the

database into human-interpretable fingerprint classes.

Any classification scheme is in principle workable so

long as different impressions of a same finger consis-

tently fall into a same class. Instead of grouping finger-

prints based on the visual appearance of fingerprint

images, we can partition the database in the feature

space into the machine-generated fingerprint classes,

in the hope that more classes can be formed. However,

there are always fingerprints located near the class

boundaries regardless of how well the database is par-

titioned. These fingerprints are likely misclassified due

to the large variations of different impressions of a

same finger. To alleviate this problem, fingerprints are

not pre-classified, but associated with numerical fea-

ture vectors. Given a query fingerprint, a fingerprint

class is then formed by retrieving a portion of finger-

prints from database whose feature vectors are similar

or have small distance to that of the query fingerprint.

Hence, this scheme is also called ‘‘continuous classifi-

cation’’ [1, 11, 12].

Orientation field is often used to construct

the numerical feature vector consisting of local

orientations [4, 11–13]. Note that an orientation

angle y is a periodic variable with a period of 180∘

rather than 360∘ and has discontinuity at �90∘ or 0∘

and 180∘. The smallest and the largest angles in a

period do not refer to two orientations far away but

rather close to each other. The distance between two

orientations yp and yq cannot be naively measured

by jyp�yq j , but rather by min( jyp�yq j ,180∘�jyp�yq j). Thus, the distance between two feature

vectors cannot be computed by simple arithmetic

such as Euclidean distance. To simplify the distance

computation, an orientation angle y is decomposed

into two component, cos(2y) and sin(2y) [1, 11, 14]so that the similarity of two fingerprints can be

measured by the convenient dot product of the two

feature vectors. This also enables to put weights on

different orientations, for example, r[cos(2y),sin(2y)], where r is the weight of orientation y. In fact,

the similarity of two feature vectors can be measured

by the consistency of the orientation differences. Thus,

a similarity measure between two feature vectors

Op ¼ ðyp1; yp2; :::; ypk ; :::Þ and Oq ¼ ðyq1; yq2; :::; yqk; :::Þ isdefined by j∑k rk exp[2j(yk

p�ykq)] j ∕ ∑k rk, where rk are

weights, exp[�] is a complex exponential function and

j � j is a magnitude operator [12, 13]. Besides the orien-

tation field, the average ridge distance over the finger-

print is also used as an auxiliary feature in some

approaches [12, 13].

Given a query fingerprint, a fingerprint class is

formed by retrieving a number of fingerprints from

the database whose feature vectors are nearest to that

of the query fingerprint. Depending on application

scenarios, different fingerprint retrieval strategies can

be applied, such as a fixed distance threshold, or a fixed

percentage of fingerprints in database to be retrieved,

or some combination of the both [12]. In an identifi-

cation system, fingerprint retrieval and exact matching

can be integrated so that the retrieval threshold

increases from a small value until the query fingerprint

is matched with one of the retrieved templates by a

matching algorithm. The threshold can increase by

a fixed step or based on a fixed number of newly

retrieved fingerprints. The incorporation of matching

in the fingerprint retrieval may greatly improve the

retrieval performance if a good matching algorithm is

applied [1, 11, 12].

The continuous classification in general needs to

compare the feature vector of a query fingerprint with

those of all fingerprints in the database. The time


443


consumption of fingerprint retrieval thus directly

depends on the database size. For large database, the

continuous classification could be time consuming. To

circumvent the one-by-one exhausting comparisons of

a query fingerprint with all templates, database is par-

titioned into clusters and hence the query fingerprint

only needs to be compared with the cluster prototypes

[12, 13]. Since in general there are always some finger-

prints near the cluster boundaries regardless of how

well the clusters are formed, it is crucial to retrieve,

instead of one, a few clusters. For the application of

automated identification, this clustering based classifi-

cation scheme is comparable to the Galton–Henry

scheme in terms of the search speed that is indepen-

dent to the database size. But the former has potential

to achieve better classification accuracy and efficiency.

Fingerprint database indexing [9, 10] is a closely

related problem to this classification scheme. Different

from the clustering based classification scheme, how-

ever, fingerprint indexing approaches [9, 10] utilize

minutia points that most automated fingerprint

matching algorithms rely on for the exact fingerprint

comparison.

Classification Performance

The performance of a fingerprint classification system is

usually measured in terms of accuracy or error

rate, efficiency or penetration rate, and speed or compu-

tational complexity. The measurements of these perfor-

mance indicators could be quite different on different

fingerprint databases. Therefore, the performance com-

parison of different classification algorithms should be

based on the same database. The NIST (National Insti-

tute for Standards and Technology) Special Database 4

is the most often used database for the classification

performance evaluation. It contains 2,000 fingerprint

pairs, uniformly distributed in the five Galton–Henry

classes (see Fig. 1). Some approaches are tested on a

reduced set (called Set 2), containing 1,204 fingerprints

extracted from the database according to the real dis-

tribution of fingerprints.

The error rate is computed as the ratio of the

number of misclassified fingerprints to the total num-

ber of samples in the test set. For a Galton–Henry

classification system, a fingerprint is misclassified if it

is placed in a class different from the human assigned

one as the true class membership of a fingerprint is

determined by human experts. For a system that is

based on the machine-generated fingerprint classes, a

query fingerprint is misclassified if the retrieved subset

from database contains no fingerprint originating from

the same finger as that of the query fingerprint.

The error rate of a classification system in general

should be reported as a function of the penetration

rate that is a performance indicator of the classification

efficiency.

The classification efficiency is measured by the

penetration rate defined as the average ratio of the

number of fingerprints in a class to the total number

of samples in the database [1, 11, 12]. If qi represents

the ratio of the number of fingerprints in class i to the

total number of samples in database and pi is the class

occurrence probability, the penetration rate is calculat-

ed by ∑i pi qi. For example, for the five Galton–Henry

classes with the occurrence probabilities of 0.037,

0.029, 0.338, 0.317, and 0.279, respectively, the pene-

tration rate of a error free classifier (qi ¼ pi) is 0.2948,

which lies between the penetration rates of 0.25 and

0.3333 for the four and three equal-sized classes,

respectively.

Figure 3 illustrates the tradeoff between the classi-

fication error rate and the penetration rate of three

techniques tested on two data sets. Obviously, lower

classification error rate can be achieved at higher pen-

etration rate. As higher classification accuracy and

efficiency are measured by lower error rate and lower

penetration rate, respectively, a lower curve indicates

a better classification performance. Table 1 shows the

classification results of some Galton–Henry scheme

based approaches (the first seven rows) and the

clustering based approach (the last two rows). All

results are obtained from NIST Special Database 4.

Some approaches are tested on the Set 2 and some

approaches are tested on the second half of the data-

base because they use the first half of the database to

train their programs. Classification performance on

the real distributed fingerprints is also resembled by

the ‘‘weighted classes’’ shown in the third and the fifth

columns. Note that Fig. 3 and Table 1 do not serve as a

direct comparison between different algorithms due to

different experimental settings and rate calculations.

More information about the classification perfor-

mances of these approaches can be found in the re-

spective references [4–8, 10–12, 14].


444


Summary

The development of automatic fingerprint identifica-

tion system for large database is a challenging task due

to both accuracy and speed issues. Fingerprint classifi-

cation as a tool to narrow down the searching space of

exact matching can alleviate these difficulties. A lot of

different techniques have been developed to automate

the Galton–Henry classification scheme, thanks to its

human-interpretability and rigid segmentation of a

database. However, the Galton–Henry classifica-

tion scheme that partitions the database into human-

interpretable fingerprint classes does not reduce the

search space significantly. The database partition

based on the machine-generated fingerprint classes

seems to be a more promising alternative for efficient

reduction of the search space. For a classification sys-

tem that requires high accuracy, a fingerprint rejection

engine can be applied to exclude poor quality finger-

prints at a price of lower classification efficiency.

Fingerprint Classification. Table 1 Classification error rates in % on NIST Special Database 4 of some Galton–Henry

scheme based approaches (the first seven rows) and the clustering based approach (the last two rows)

Source5 classes

Five weightedclasses Four classes

Four weightedclasses

Test setP.R. = 20 P.R. = 29.5 P.R. = 28 P.R. = 29.7

Candela et al. [4] – – 11.4 6.1 Second half

Karu and Jain [6] 14.6 11.9 8.6 9.4 Whole

Jain et al. [5] 10 7.0 5.2 – Second half

Cappelli et al. [11] – 12.9 – – Set 2

Cappelli et al. [14] 7.9 6.5 5.5 – Second half

Senior [8] – – – 5.1 Second half

Park and Park [7] 9.3 – 6.0 – Whole

Jiang et al. [12] 5.3 3.3 3.5 3.2 Whole

Jiang et al. [12] 4.7 2.9 3.2 2.8 Set 2

The penetration rate is shown by the value of P.R. in %. In the columns of ‘‘weighted classes’’, error rates of different classes are weighted

by the class occurrence probabilities in the calculation of the total error rate

Fingerprint Classification. Figure 3 Classification error rate against penetration rate: (a) approach A in [ 11] and B

in [12] tested on the the NIST Special Database 4 Set 2 containing 1,204 fingerprint pairs; (b) approach B in [12] and C in

[10] tested on the the second half of the NIST Special Database 4 containing 1,000 fingerprint pairs.


445


Further research efforts are necessary to improve the

classification performance.

Related Entries

▶ Fingerprint Feature Extraction

▶ Fingerprint Indexing

▶ Fingerprint Recognition Overview

▶ Identification and Authentication

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Fingerprint Classiﬁcation F - NTU · Au1 Fingerprint Classiﬁcation ... shows a stable and reliable feature and is employed in ... struggle against the large intra-class and small