Title: Instructors: Dr. George Bebis and Dr. Ali Erol.
1Fingerprint ClassificationHandbook of
Fingerprint RecognitionChapter 5 (5-1 and
5-2) Fingerprint Classification by Directional
Image PartitioningRaffaele Cappelli, Alessandra
Lumini,Dario Maio and Davide Maltoni. IEEE
TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE
INTELLIGENCE, vol. 21, no. 5, pp. 402-421, 1999.
- Instructors Dr. George Bebis and Dr. Ali Erol.
- Presented by Milind Zirpe.
- CS 790Q (Fall 2005).
2Overview
- Fingerprint Classification
- Introduction.
- Main classification techniques.
3Introduction
- Need for fingerprint classification
- Database of fingerprints may be very large (e.g.
several million fingerprints). - Leads to long response time and hence unsuitable
in real time applications. - To reduce the number of comparisons.
4Introduction
- What is fingerprint classification ?
- Fingerprint classification refers to the problem
of classifying a fingerprint to a class in a
consistent and reliable way. - An approach
- A common strategy is to divide the fingerprint
database into a number of bins, based on some
predefined classes. A fingerprint to be
identified is then required to be compared only
to the fingerprints in a single bin of database
based on its class.
5Introduction
- Galton-Henry classification (Galton, 1892 and
Henry, 1900).
6Introduction
- A difficult pattern recognition problem
7Introduction
- A difficult pattern recognition problem
8Classification Techniques
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10Classification Techniques
- 1. Rule-based approaches
- Classification according to the number and
position of the singularities (commonly used by
human experts for manual classification).
11Classification Techniques
- a. Kawagoe and Tojo (1984)
- Derive a coarse classification using type and
position of singular points. - Finer classification is obtained by tracing the
ridge line flow.
12Classification Techniques
- b. Karu and Jain (1996)
- An iterative regularization (smoothening
orientation image with a 3x3 box filter) is done
until a valid number of singular points are
detected. This allows reducing noise and thus
improves classification accuracy.
- Criteria for differentiating between tented
arches and loops - Connect the two singularities with a straight
line and measure the avg. difference between the
local orientations along the line and the slope
of the line. A fingerprint is classified as a
tented arch if
13Classification Techniques
- c. Hong and Jain (1999)
- A more robust technique is proposed the authors
introduced a rule-based classification algorithm
that uses the number of singularities along with
the number of recurring ridges found in the
image. - The combination of these two distinct features
leads to a performance better than that found in
Karu and Jain (1996).
14Classification Techniques
- Problems with Rule-based approaches
- Although simple, some problems arise in presence
of noisy or partial fingerprints, where
singularity detection can be extremely difficult.
(Addressed to some extent by Karu and Jain (1996)
approach). - May work well on rolled (nail to nail)
fingerprint impressions scanned from cards, but
are not suitable for dab (live-scan) fingerprint
images, because delta points are often missing in
these types of images.
15Classification Techniques
- 2. Syntactic approaches
- A syntactic method describes patterns by means of
terminal symbols and production rules. - Terminal symbols are associated to small groups
of directional elements within the orientation
image and represent a class. - A grammar is defined for each class and a parsing
process is responsible for classifying each new
pattern (Fu and Booth, 1986a, b). -
16Classification Techniques
- a. Rao and Balck (1980)
- A ridge line is analyzed and represented by a set
of connected lines. - These lines are labeled according to the
direction changes, thus obtaining a set of
strings that are processed through ad hoc
grammars or string-matching techniques to derive
the final classification.
17Classification Techniques
- Problems with Syntactic approaches
- Due to the great diversity of fingerprint
patterns, syntactic approaches require very
complex grammars whose inference requires
complicated and unstable approaches.
18Classification Techniques
- 3. Structural approaches
- Based on the relational organization of
low-level features into higher-level structures.
This relational organization is represented by
means of symbolic data structures (viz. trees and
graphs), which allow a hierarchical organization
of the information (Bunke, 1993).
19Classification Techniques
- a. Maio and Maltoni (1996)
- The directional image is partitioned into several
homogenous regular-shaped regions, which are used
to build a relational graph summarizing the
fingerprint macro-features. - Directional image is computed, over a discrete
grid 32x32, using a robust technique proposed by
Donahue and Rokhlin (1993). - A dynamic clustering algorithm, Maio and Maltoni
(1996), is adopted to segment the directional
image. - A relational graph is built by creating a node
for each region and an arc for each pair of
adjacent regions. - An inexact graph matching technique, derived from
Bunke and Allermann (1983), is used to compute a
distance vector between the graph and each
class prototype graph.
20Classification Techniques
Class prototype graphs
Fig.3. Main steps. The intermediate results
produced during the classification of a Left Loop
fingerprint are shown.
21Classification Techniques
- Advantages
- The relational graphs are invariant with respect
to displacement and rotation of image. - The technique neither requires any position
alignment nor any normalization. - In principle, can be directly used for
classification of partial fingerprints (i.e.,
matching a graph with a sub graph). - Problems with Structural approaches
- It is not easy to robustly partition the
orientation image into homogenous regions,
especially in poor quality fingerprints.
(Resolved to some extent by Cappelli et al.
(1999) using template-based matching).
22Classification Techniques
- 4. Multiple classifier-based approaches
- Different classifiers offer complementary
information about the patterns to be classified.
This motivates combining of different approaches
for the fingerprint classification task.
23Classification Techniques
- a. Candela et al. (1995)
- Based on Neural Network and Rule-based
approaches. - The system is called as PCASYS (Pattern-level
Classification Automation SYStem). - A probabilistic neural network is coupled with an
auxiliary ridge tracing module, specifically
designed to detect whorl fingerprints.
24Classification Techniques
Fig. A functional scheme of the PCASYS.
25Classification Techniques
- b. Jain, Prabhakar, and Hong (1999)
- Two stage classification strategy based on
Statistical and Neural Network approaches. - Stage 1 A k-nearest neighbor classifier is used
to find the two most likely classes from a
FingerCode feature vector (section 4.6). - Stage 2 A specific neural network, trained to
distinguish between the two classes, is utilized
to obtain the final decision. A total of 10
neural networks are trained to distinguish
between each possible pair of classes.
26Classification Techniques
27Fingerprint Classification by Directional Image
Partitioning Raffaele Cappelli, Alessandra
Lumini,Dario Maio and Davide Maltoni. IEEE
TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE
INTELLIGENCE, vol. 21, no. 5, pp. 402-421, 1999.
28Overview
- Fingerprint Classification by Directional Image
Partitioning - Introduction.
- The new approach.
- Fingerprint retrieval.
- Experimental results.
- Conclusion.
29Introduction
- The relational graph approach has some problems
in obtaining analogous segmentation from similar
directional images. - Influenced too much by local ridge-line
orientation changes, start point of clustering
routines. - The new approach uses dynamic masks for
directional image partitioning. - It is translation and rotation invariant and does
not require the singularities to be detected.
30Introduction
Fig. 4. The segmentation of two Left Loop
fingerprints.
31The New Approach
- Overview of the new approach.
- Directional image computation and enhancement.
- Dynamic mask definition.
- Directional image partitioning with Dynamic
masks. - Generation of a set of Prototype masks.
- Classification.
32Overview of the new approach
- The basic idea of the new approach is to perform
a guided segmentation of the directional image
with the aim of drastically reducing the degrees
of freedom during the partitioning process,
conferring stability to the solutions. - A set of dynamic masks, directly derived from the
most common fingerprint classes, are used to
guide the partitioning. - The inexact graph matching step is simplified and
embedded in the segmentation step.
33Overview of the new approach
Fig. 6. Classification of a Left Loop fingerprint
by means of the dynamic masks approach.
34Directional image computation and enhancement
- Directional image computation
- The finger area is separated from the background
and its quality is improved by a filtering in the
frequency domain. - The R.M. Stock and C.W. Swonger (1969) method is
applied to calculate directional elements. Each
element is represented by a vector v. - Directional image enhancement
- Regularization
- Regularization of directional elements by local
averaging on 3x3 windows W.
35Directional image computation and enhancement
- Directional image enhancement (contd.)
- Attenuation
- Attenuation of the border elements by applying a
Gaussian-like function which progressively
reduces the element magnitude moving from the
center towards the borders.
where distc(v) returns the distance in blocks of
v origin from the directional image center and s
determines the scale of the Gaussian function.
36Directional image computation and enhancement
- Directional image enhancement (contd.)
- Strengthening
- We use a strengthening function (str) which
increases the significance of each element
according to the irregularity degree of its 3x3
neighborhood, without requiring the singularities
to be explicitly detected.
returns 0 if all the vectors are parallel to each
other and its value approaches 1 when discordance
increases.
The resulting directional image is made up of
vectors ve such that
where is a weighting factor.
37Directional image computation and enhancement
Fig. 7. Enhancement of a directional image the
map in the arrow-box shows the most irregular
regions as revealed by the str function. The
parameters are s 9.6 and l 112.
38Dynamic mask definition
- Dynamic masks have been introduced in order to
decrease the degrees of freedom during the
partitioning process. - Each mask is characterized by a set of vertices
defining the borders of the regions which
determine the segmentation. - Some vertices can be locally moved to best fit
the fingerprint image singularities, which can
occupy different positions within fingerprints of
the same class.
Fig. 8. The singularity positions in three
different Left Loop fingerprints.
39Dynamic mask definition
- Formally, a dynamic mask is defined as a 6-tuple
- M ,
- where
- V is a set of vertices p.
- P is a set of polygonal regions
whose vertices are in V. - is a relation, encoding the dependency
of the dependent vertices from the mobile ones.
Each dependent vertex is anchored to exactly one
mobile vertex. - encodes a relation between some
region pairs. For each pair Pi, Pj, whose
orientation difference is significant,
the triplet . - is a function which associates to each
mobile vertex a mobility window which limits the
vertex movements during the mask adaptation. - is a function which indicates, for
each pair in the dependent vertex
movement on the basis of the corresponding mobile
vertex movement.
40Dynamic mask definition
Fig. 9. An example of dynamic mask definition.
Fixed vertices are denoted by empty circles, the
mobile ones by black circles, and the dependent
ones by gray circles. The dashed boxes denote the
mobility windows associated to the mobile
vertices. An arrow from a mobile vertex pi to a
dependent vertex pj indicates the dependence of
pj on pi.
41Directional image partitioning with Dynamic masks
- Let MT,Q be the steady mask obtained by the
dynamic mask M as a result of the following
transformations - a global rotation-displacement T
where and denote the global mask
displacement and denotes the global mask
rotation. - a set of mobile vertex displacements Q (dx1,
dy1), (dx2, dy2), ... (dxi, dyi) denotes the
displacement of the vertex pi with respect to its
initial position. - The application of a steady mask MT,Q to a
directional image D consists in superimposing
MT,Q on D and deriving a segmentation R R1,
R2, ..., Rn where each region Ri is made up of
the directional elements internal to the polygon
Pi.
42Directional image partitioning with Dynamic masks
- The cost Csm(MT,Q, D) of the application of MT,Q
to D is given by the sum of two terms
where
First term Var(Ri) is proportional to the
variance of the directional elements in Ri and C0
is a parameter which introduces a penalty
proportional to the number of regions in M in
order to balance the possibility of obtaining
lower costs by segmenting the directional image
into several small regions. Second term
returns the difference between the avg.
orientations of regions Ri and Ri returns
the difference between qi, qj µ is the weight
of the orientation difference contribution, and
returns the number of triplets in .
43Directional image partitioning with Dynamic masks
- The application cost of a dynamic mask M to a
directional image D is computed by determining
the minimum cost over all the possible steady
masks MT,Q
44Directional image partitioning with Dynamic masks
Fig. 10. Adaptation of the mask defined in Fig. 9
to three different images of the same Left Loop
fingerprint.
45Generation of a set of Prototype masks
Fig. 11. Prototype mask creation. The mask area
is larger than the directional image to allow the
border elements to be considered during the mask
displacement.
46Generation of a set of Prototype masks
Fig. 11 (contd.). Prototype mask creation. The
mask area is larger than the directional image to
allow the border elements to be considered during
the mask displacement.
47Generation of a set of Prototype masks
Fig. 12. The five prototype masks derived from
the classes Arch, Left Loop, Right Loop, Tented
Arch, and Whorl. The vertex positions, the
mobility windows, and the dependencies on mobile
vertices are graphically shown.
48Generation of a set of Prototype masks
Fig. 12 (contd.). Example of application of each
mask to a fingerprint belonging to the
corresponding class.
49Classification
- Let Mi, i 1..s be the prototype masks and D the
directional image to be classified the feature
vector wD resulting from the mask application is - where low component values denote high
similarity with the corresponding prototype mask. - wD can be normalized as
- The normalization enables
- working within the fixed range 0, 1 this makes
fingerprint indexing through spatial data
structures easier. - dealing with differently contrasted images The
image contrast is related to the magnitude of the
directional elements hence, it can determine an
increase or a reduction of all the costs.
50Classification
Fig. 13. The figure shows the segmentation
obtained by applying the prototype masks defined
in Fig. 12 to some sample fingerprints (only one
example is provided for each class) the
corresponding normalized feature vectors are
shown on the right in the form of histograms.
51Classification
Fig. 13. (contd.)
52Fingerprint retrieval
- Exclusive classification
- A neural network or a statistical classifier can
be used to properly classify vectors . - Continuous classification
- itself can be used as an access key for
similarity searches. (Each fingerprint is
characterized with a numerical vector). - In order to evaluate the efficiency of continuous
vs. exclusive classification for latent
fingerprint retrieval, two different
methodologies were proposed (A. Lumini, D. Maio,
D. Maltoni, 1997), named A and B. - Four different strategies AE, AC, BE, BC.
53Fingerprint retrieval
- Methodology A
- Methodology A assumes an error-free
classification, so the search is restricted to
the database fingerprints resembling analogous
classification characteristics. - AE
- The strategy AE can be implemented by searching
the whole corresponding class of the latent
fingerprint. - The average portion of database considered
- The average retrieval error
Where, Pd(i) represents the database fraction
involved in the retrieval of a fingerprint of
class i and Pc(i) is the weighting factor
representing the probability to classify a latent
fingerprint as i.
Where, Pdc(ji) represents the conditional
probability that a database fingerprint,
corresponding to a latent fingerprint classified
as i, has been classified j in the database.
54Fingerprint retrieval
- Methodology A
- AC
- The strategy AC can be implemented by searching
among those fingerprints which are less far from
the feature vector w of the latent fingerprint
than a fixed tolerance ?. - Given a fixed ?,
- The average portion of database considered C?(AC)
is determined by the average number of
fingerprints inside the hyper-sphere with radius
?, centered in the latent fingerprint. - The average retrieval error E?(AC) is determined
by the average number of missed retrievals inside
the search area.
55Fingerprint retrieval
- Methodology B
- Methodology B allows for misclassification to be
taken into account to this aim, the search is
carried out incrementally over the whole
database, avoiding any possible retrieval error. - This methodology requires the search to be
terminated when a human expert finds a real
correspondence between the latent fingerprint and
a database fingerprint that has already been
considered. - BE
- The strategy BE can be implemented by starting
the search from the latent fingerprint class, and
incrementally extending it to the other classes. - BC
- The strategy BC can be carried out by processing
fingerprints according to their distance from the
latent fingerprint feature vector w.
56Experimental results
- Databases used
- NIST Special Database 4 (Db4) contains 2,000
fingerprint pairs, uniformly distributed in the
five classes, in order to resemble a real
distribution. - NIST Special Database 14 (Db14) contains 27,000
fingerprint pairs randomly taken, thus
approximating the real fingerprint distribution
only the last 2,700 fingerprint pairs have been
employed in the simulation. - The first 2,000 fingerprints of Db14 have been
used as training set to derive the set of
prototype masks and to optimize the parameters. - MASK the dynamic mask method introduced in this
paper. - LUMINI the continuous classification approach
described in A. Lumini, D. Maio, D. Maltoni
(1997). - PCASYS the exclusive approach by NIST (G.T.
Candela, et al., 1995).
57Experimental results
Fig. 14. MASK results over Db4 (a) and Db14 (b)
the average portion of database considered C?(AC)
and the average retrieval error E?(AC) are
plotted as a function of ?.
58Experimental results
Fig. 15. Trade-off C?(AC)/E?(AC) varying ? for
the two continuous approaches MASK and LUMINI.
The point denotes C(AE)/E(AE) for the
exclusive classification approach PCASYS.
59Experimental results
TABLE 1 STRATEGY AC COMPARISON BETWEEN LUMINI
AND MASK.
60Experimental results
TABLE 2 COMPARISON AMONG PCASYS, LUMINI, AND
MASK FIXING THE AVERAGE PORTION OF DATABASE READ.
TABLE 3 COMPARISON BETWEEN THE AVERAGE
PERCENTAGES OF DATABASE SEARCHED (METHODOLOGY B).
61Experimental results
TABLE 5 CLASSIFICATION OF SOME FINGERPRINT
IMAGES SUBMITTED TO ARTIFICIAL PERTURBATIONS.
(Robustness).
62Experimental results
TABLE 8 AVERAGE TIME SPENT FOR THE MAIN
PROCESSING STEPS.
63Conclusion
- Dynamic masks have been defined as a powerful
instrument for a robust segmentation. (Noisy and
partial fingerprint images). - The experimental results prove the accuracy and
robustness of the new method. - The comparison with other techniques demonstrates
its superiority for the continuous classification
task, especially if fingerprints are classified
only for improving the retrieval efficiency. - Continuous classification does not enable to
accomplish some tasks to be carried out, such as
fingerprint labeling according to a given
classification scheme.
64Thank you.