Fingerprint identification technique

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME
308
FINGERPRINT IDENTIFICATION TECHNIQUE
BASED ON WAVELET-BANDS SELECTION FEATURES (WBSF)
Dr. Mustafa Dhiaa Al-Hassani,
Mustansiriyah University, Baghdad-Iraq
Dr. Abdulkareem A. Kadhim,
Al-Nahrain University, Baghdad-Iraq
Dr. Venus W. Samawi,
Al al-Bayt University, Jordan
ABSTRACT
The paper is concerned with the use of fingerprint (FP)features for protection against
unauthorized access. Wavelet features for both closed and open-set FP recognition are studied
here to verify persons' identity. Fingerprints of 49 persons (32-authorized and 17-unauthorized)
were taken as testing data. Each authorized person is asked to give 10-instances of his right
forefinger print. In the closed-set FP recognition, the obtained recognition rates are below 90%
due to the imperfections in the FP images that negatively affect the recognition rate.
Preprocessing operations such as: noise-removal, segmentation, normalization and binarization
are considered to improve the resulting recognition rates. A method that relies on a new
selection process for wavelet decomposition bands is proposed, which enhance the recognition
rates further to get about 100% in some favorable conditions. The results have shown that the
wavelet descriptors using the proposed Wavelet-Bands Selection Features (WBSF) are efficient
representation that can provide reliable recognition for large input variability. The open-set FP
verification mode is also presented for 290 trials from 29 persons, where the obtained
verification rates are greater than 97% for both Euclidean and city-block distance measures.
Keywords: Fingerprint Recognition, Fingerprint Verification, Biometric, Feature Extraction,
Wavelet Transform, Wavelet-Bands Selection Features.
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
& TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 3, May-June (2013), pp. 308-323
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
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IJCET
© I A E M E

309
I. INTRODUCTION
Due to the escalating level of security breaches and transactions frauds, the need for
highly secure identification and personal verification technologies becomes essential. The key
task of an automated security system is to verify that the users are in fact those who claim to
be. The use of biometric information has been used widely for both person identification and
security applications. Biometric-based solutions are able to provide confidential financial
transactions and personal data privacy [1]. A biometric can be described as a measurable
physical and/or behavioral trait that can be captured and used to verify the identity of a person
[2].
FP recognition is a rapidly evolving technology that has been widely used in forensics
such as criminal recognition and prison security, and has widely adopted in a broad range of
civilian applications such as national ID card, airport check-in, border control, driver’s-license
authenticity, computer network logon, physical access control, electronic banking, personal
authentication,… etc [3, 4, 5].
The real significance of FP is based mainly on the following principles: 1) People can't
"forget" their fingerprints, 2) It is easy to authenticate, 3) Impossible to deny, 4) It is a physical
characteristic instead of something to be remembered or carried around; it is less susceptible to
misuse than other authentication measures like passwords or credit cards, 5) Unchangeable, 6)
Unforgeable, ... etc [6, 7].
Several researches in the field of FP recognition/verification were developed and
receive a great deal of attention among many researchers using of wavelet transform and other
feature extraction methods: Priti and Priyadarshan [4] introduced a FP verification using Haar
wavelet transform method. The system was tested on a Biolab Database of 2160 FP images.
The obtained verification accuracy is 82.08% even by rotating each FP image from 00
to 3600
.
Eriksson [7] illustrated that silicon FP scanners produce good quality images, this work
presents two main approaches to minutia detection in FP images, binary detection and direct
grayscale detection. The results are tested on 6283 fingerprints collected by the Verdicom
FPS110 silicon FP scanning device and they reported about 92% classification accuracy. Saeed,
Tariq, and Jawaid [8] improved a fingerprint image enhancement technique using Gabor
wavelets. The system was tested on Fingerprint Verification Competition (FVC) 2004 database.
Experimental results show that the proposed algorithm proved to be effective in enhancing the
fingerprint image quality, where the achieved accuracy is 97.14%. In [9] the authors presented
minutiae based approach to FP identification and verification. The technique is based on the
extraction of minutiae from the thinned, binarized and segmented version of a FP image. The
system was tested on the FVC2000 database using low cost capacitative FP scanners, which
contains 800 fingerprints from 110 different fingers. The system was implemented using
Matlab 6.5 and the time taken for processing a single FP is 12 seconds that implies accuracy
92%.
II. AIM OF THE WORK
This work aims to design and build a secure, fast, reliable, and accurate identification
system for access control that is capable of distinguishing the authorized persons from others
(i.e., impostors), and then gives only the authorized persons a privilege or an access right to the
facility that need to be protected from the intrusion of unauthorized persons.

310
In this paper, a novel Wavelet feature-set (WBSF) is proposed for representing FP pattern.
FP recognition and verification is also to be investigated for open and closed-set models.
III. THE PROPOSED FP RECOGNITION SYSTEM MODEL
In general, the function of FP systems can be separated into several distinct phases,
which include sensing or reading FP, preprocessing operations, FP registration, feature
extraction followed by a classification search and decision rule [1, 10]. The block diagram for
the proposed FP recognition system model, shown in Fig. (1), illustrates that the input FP
image is passed through four preprocessing operations (noise-removal, segmentation,
normalization and binarization) prior to feature extraction phase [1].
Fig. (1): Block-Diagram of the proposed FP Recognition
System Model
Features are extracted from wavelet domain, using the classical pyramidal Wavelet
transform decomposition followed by the features extracted from the proposed Wavelet-Bands
Selection Features (WBSF), as shown in the design of the proposed system to recognize a
query FP image by comparing it with a training database of F Preferences during a pattern
matching phase. Finally, the distance measures (Euclidean and City-block) are used to calculate
the difference between the feature vector of the query FP with the feature vector of the
potential FP in the database. The next subsections will cover the details of each stage [1].
A. Input FP Image
Figure (2) illustrates some examples of input FP images used for training or testing modes
to our system model from the right forefinger of different persons (P1, P2, …, P6) [1].

311
Fig.(2):Example of 6–FP images from different Persons
B. Preprocessing of the FP Images
FP images are rarely of perfect quality. They may be degraded and corrupted with
elements of noise due to many factors including variations in skin and impression conditions. It
must also overcome fingers pressed too hard or too gently to get an acceptable image. Getting
an acceptable image is probably the most important factor in determining fingerprints
genuineness. Bad quality prints can result in unsuccessful recognition attempts or even worse,
erroneous logins. Thus, image enhancement techniques are employed prior to feature extraction
to reduce the noise and enhance the definition of ridges against valleys. A number of
processing techniques adopted in this system model are applied in the following sequence [1]:
Noise-Removal (using Mean or Gaussian filter), Segmentation (foreground/background
separation), Normalization (to reduce the effect of non-uniform intensities and improving
image quality by stretching its histogram), and Binarization (using local mean). Figure (3)
illustrates the effects on a sample FP image [1].
Fig. (3): The sequence of preprocessing steps for FP image sample
P1 P2 P3
P4 P5 P6

312
C. Database Construction
FP identification system (both FP recognition and FP verification) depends on FP samples
as input data. In this work, database samples were collected in two modes of operation:
• Closed-set FP recognition mode
• Open-set FP verification mode
In order to evaluate the recognition performance of the proposed system model, each user
of the system (to be considered as authorized one) has been asked to provide his forefinger
print for a maximum of 10 prints from the same forefinger (i.e., 10 instances), as shown in Fig.
(4). The number of repetition R ( 1≤ R ≤ 10 )can be considered as training set during an
enrollment phase to train the fingerprints model of authorized persons, and the other ( 10 – R )
repetitions are considered for testing during a matching phase to classify them with those
training references in the database [1].
Fig. (4): Demonstrates 6-FP instances from the same Person P2
The data were collected from 32 different persons, 18 males and 14 females, in a closed set FP
recognition mode (i.e. 320 samples). As a result, the total database size of FP samples for this
mode is [1]:
…… (1)
…… (2)
…… (3)
In the open-set FP verification mode, up to 290 trials from different persons (i.e., authorized
and unauthorized) are taken. This is performed in order to study the system behavior and to
select the optimal threshold for user verification.
D. Feature Extraction
The process of extracting some numerical measurements from raw input patterns by
constructing a new "smaller" set of features from the original feature set of patterns (i.e.
rsonsNo. of PeizeTotal DB S ×= 10
PersonsTrainingofNo of.NoRReferences. ×=
PersonsSamplesTestofNo of.NoR)(10. ×−=
P2,1P2,2 P2,3
P2,4 P2,5 P2,6

313
)(
1
(t) ψψ ,
j
kt
jkj
−
=
reducing dimensionality) is referred to as feature extraction[11].In this work, features are
extracted from the spectral properties of the wavelet transform.
Wavelet transform breaks an image down into four sub-sampled images, and then
analyze each component with a resolution matched to its scale. The forward and inverse
continuous wavelet transform ofx(t) with respect to the basis function or wavelet (t)ψ k,j at scale j
(j>0) and time delay k is written as follows [4, 12 –14]:
..…(4)
..…(5)
where
..…(6)
and (t)ψ is the mother wavelet.
After converting the input FP image from its lowest-level of pixel data into higher-level
representation of wavelet coefficients, [1],a set of wavelet features that represent the input FP
image can be extracted by recursively decomposing sub images in the low frequency channels
using Algorithm-1 as shown below:
Algorithm-1:The Classical Pyramidal Wavelet Transform Decomposition [1, 15]
Step1: Decompose a given textured image with 2-D wavelet transform into 4 sub images, as
indicated in Fig. (5) (the image is divided into four sub bands after wavelet transform:
horizontal, vertical, diagonal subimages and low resolution subimage).
Fig. (5): Three-level Wavelet Decomposition
LL3 LH3
LH2
HL3 HH3
LH1
HL2 HH2
HL1 HH1
∫= dt)t()t(x)(W ψ k,jk,j:CWTForward
∫∫=
k j
djdk)t()k,j(W)t(x ψ k,j:CWTInverse

314
Step2: 1) Calculate the Mean Absolute Value (M.A.V.) feature for each decomposed image, as
follows [15]:
…… (7)
where
j)x(i, is the decomposed image, with Mi ≤≤1 and Nj ≤≤1 . M is the subimage height
and N is the subimage width.
2) Calculate the Standard Deviation (S.D.) feature for j)x(i, , as shown below [15, 16]:
…… (8)
The size of the smallest subimages should be used as a stopping condition for the iterative
decomposition process. It is also worthwhile to point out that the above pyramidal wavelet
transform decomposition takes no more space to store the wavelet coefficients than it does to
store the original image.
E. Pattern Matching
The resulting test template, which is an N-dimensional feature vector, is compared
against the stored reference templates to find the closest match. The process is to find which
unknown class matches a predefined class or classes. For the FP recognition task, the unknown
FP is compared to all references in the database. This comparison can be done through
Euclidean (E.D.) or city-block (C.D.) distance measures [17], shown below:
…… (9)
….. (10)
where A and B are two vectors, such that A = [a1 a2 … aN]and B = [b1 b2 … bN].
The primary methods for the discrimination process are either to measure the difference
between the two feature vectors or to measure the similarity. In our approach the minimum
distance classifier, by measuring the difference between the two patterns, is used for FP
recognition. This classifier assigns the unknown pattern to the nearest predefined pattern. The
bigger distance between the two vectors, is the greater difference. On the other hand, the
identity of the unknown FP was verified by considering the best matched reference in the
database where their distance is lower than a certain threshold [17, 18].
IV. EXPERIMENTAL RESULTS
The recognition rate (R.R.) is defined as the ratio of correct identified fingerprints to the
total number of test samples which corresponds to a nearest neighbor decision rule.
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315
..… (11)
Many experiments and test conditions were accomplished to measure the performance
of the proposed system with different criterions concerning: wavelet decomposition level
selection, FP noise-removal, segmentation, normalization, binarization, the effect of the
proposed WBSF on the overall recognition/verification rates when compared with the classical
pyramidal wavelet transform decomposition method.
A. The Selection of Wavelet Level
In order to select the best level of wavelet decomposition for the system, this test is
performed. Different Daubechies wavelet functions are considered as shown in Table-1.
Table-1: Recognition rates for different levels of wavelet decomposition using (M.A.V.)
feature
The wavelet levels considered for each function is varied from 1 to 5. The number of
wavelet features for the first level is 4, and each progressing in wavelet level by iteratively
decomposing the low resolution sub image will correspond increasing in features length by 3.
For each training or testing, five repetitions for each FP are considered which resulted in 160
samples.
It is clear from Table-1 and its corresponding chart Fig. (6), that Level-4 is the most
appropriate level for feature vector construction where all the recognition ratesare the highest
among almost all Daubechies functions.
%
TestedSamplesNo. ofTotal
FPIdentifiedCorrectlyofNo.
R.R. 100×=
Wavelet
Function
Level-1 Level-2 Level-3 Level-4 Level-5
D2 41.875 55.000 75.000 76.875 76.875
D4 49.375 60.625 79.375 84.375 77.500
D6 46.250 65.000 82.500 84.375 83.750
D8 41.875 64.375 84.375 86.250 83.125
D10 43.125 65.000 84.375 87.500 86.875
D12 45.625 65.000 84.375 89.375 85.000
D14 44.375 63.750 84.375 87.500 85.000
D16 43.750 65.000 82.500 83.750 85.625
D18 44.375 65.000 85.000 86.875 86.250
D20 41.875 66.875 84.375 87.500 82.500

316
Fig. (6): Recognition rates for different levels of wavelet decomposition using (M.A.V.)
feature
B. Segmentation of Low-pass Filtered FP images
After determining the appropriate wavelet decomposition level, a number of preprocessing
steps were performed to enhance image quality. The first step is to remove the noise from the
input FP images using the Gaussian-filter, and then separating the foreground regions from the
background regions in a FP image. Figure(7) shows the resulting recognition rates when
Euclidean distance measure and M.A.V. were used.
Fig.(7): Effects of Segmentation on recognition rates for the Gaussian-filtered FP
It is clear from Fig. (7), that the segmentation process enhances all the recognition rates
for the Gaussian-filtered FP images; where about (96%) recognition rate is achieved using D8.
On the other hand, when segmentation is not used, all recognition rates are below 90%.
65
70
75
80
85
90
95
100
D2 D4 D6 D8 D10 D12 D14 D16 D18 D20
Daubechies Wavelet functions
RecognitionRate%
Gaussian Filter with Segmentation
40
50
60
70
80
90
100
D2 D4 D6 D8 D10 D12 D14 D16 D18 D20
RecognitionRate%
Level1 Level2 Level3
Level4 Level5

317
C. Binarization of Normalized FP images
Further improvements in recognition rates can be achieved when converting the
normalized FP images (using histogram stretching) from gray-scale to binary as shown in
Table-2. This increases the contrast among the ridges and valleys of FP.
Table-2: Recognition rates after Binarization of the Normalized FP images using
(M.A.V. and S.D.) features
Wavelet
Function
E. D. C. D.
M.A.V. S.D. M.A.V. S.D.
D2 90.000 96.250 87.500 94.375
D4 95.625 97.500 95.625 97.500
D6 96.250 99.375 96.875 98.750
D8 98.750 100.00 98.750 99.375
D10 95.625 100.00 96.875 98.750
D12 99.375 99.375 97.500 99.375
D14 96.250 99.375 96.875 100.00
D16 98.125 100.00 98.125 100.00
D18 98.750 100.00 98.750 99.375
D20 98.125 99.375 100.00 99.375
The results of Table-2 obviously indicate the highly enhancements in all recognition
rates after applying the binarization step to the normalized FP images for both distance
measures when compared to previous test. Furthermore, one can deduce that the (S.D.) wavelet
feature present better results than (M.A.V.) feature using both distance measures. Figure (8)
display part of this comparison by taking only the (S.D.) wavelet feature using Euclidean
distance measure.
Fig. (8): Recognition rates for wavelet feature (S.D.) after Binarization of Normalized FP
images
76
78
80
82
84
86
88
90
92
94
96
98
100
D2 D4 D6 D8 D10 D12 D14 D16 D18 D20
RecognitionRate%
Normalization With Binarization

318
D. The Proposed Wavelet–Bands Selection Features (WBSF)
This section reviews a novel selection method for the set of wavelet features that are
well suited for recognition of FP images with the aim to improve the recognition rates. In this
method, the wavelet features extracted by means of four wavelet decomposition levels (i.e. 13
features) are combined with another (18 features) extracted from five decomposition levels
wavelet bands as shown in Fig. (9). These provide information about FP image in both
horizontal and vertical directions [1].The added features are the shaded cells shown in Fig.
(10).The final calculated 31 features are arranged in a single vector that will represent the FP
feature pattern.
Fig.(9): Demonstrates 5–decomposition levels of the 2-D wavelet transform for a FP
sample
Fig.(10): The proposed wavelet channels decomposition (5-levels) by indicating the
number of each newly selected band

319
The experimental results for the proposed WBSF system are shown in Table-3with its
corresponding Figures (11) and (12), which gives better recognition rates when compared to
those in Table-2.This is due to the fact that the proposed WBSF method for feature extraction
provides extra information to assist further in recognition.
Table-3: Recognition rates for WBSF system with different wavelet functions and
distance measures
Wavelet
Function
E. D. C. D.
M.A.V. S.D. M.A.V. S.D.
D2 95.000 98.125 93.125 97.500
D4 97.500 99.375 96.250 98.125
D6 98.125 100.00 98.125 99.375
D8 98.750 100.00 97.500 100.00
D10 100.00 100.00 100.00 99.375
D12 100.00 100.00 100.00 100.00
D14 100.00 100.00 99.375 100.00
D16 98.125 100.00 98.750 100.00
D18 100.00 100.00 100.00 100.00
D20 100.00 100.00 100.00 99.375
Fig. (11): Recognition rates for wavelet feature (M.A.V.) before and after the addition of
the proposed WBSF
88
89
90
91
92
93
94
95
96
97
98
99
100
D2 D 4 D6 D 8 D10 D12 D14 D 16 D18 D20
With Binarization WBSF
RecognitionRate%

320
Fig. (12): Recognition rates for wavelet feature (S.D.) before and after the addition of the
proposed WBSF
In WBSF, the decomposition bands involved are not only the LL bands but also the LH
and HL bands that correspond to horizontal and vertical FP image details, respectively.
E. Fingerprint Verification
The final step requires the verification of user’s identity. This is relies on the best results
obtained from the previous experiments. It is undoubtedly illustrated that (S.D.) based feature,
extracted from different wavelet bands, exhibits better results when compared to(M.A.V.).
Therefore, (S.D.) is selected to be the feature for wavelet extraction method of the FP
verification mode using WBSF features set.
Since different wavelet functions can provide recognition rates close to 100% as illustrated
in Table-3, therefore we select D20 as the wavelet function for the verification tests. A total of
290 query FP samples from 29 persons (authorized and unauthorized) are considered for open-
set FP verification mode. Different threshold values were considered, as shown in Table-4 and
5. The successful decision corresponds to the rate of accepting registered persons and rejecting
non-registered ones for all trials.
Table-4: FP verification rates for D20 using Euclidean distance
Threshold )(θ Successful Decision FAR FRR
2.60 73.4482 0.0 26.5517
2.85 78.2758 0.0 21.7241
3.10 83.1034 0.0 16.8965
3.35 91.7241 0.0 8.2758
3.60 94.1379 0.0 5.8620
3.85 97.2413 0.6896 2.0689
4.10 96.5517 2.4137 1.0344
4.35 95.1724 3.7931 1.0344
4.60 92.4138 6.8965 0.6896
4.85 89.3103 10.0000 0.6896
5.10 86.5517 12.7586 0.6896
5.35 82.4138 16.8965 0.6896
5.60 78.6206 21.0344 0.3448
88
89
90
91
92
93
94
95
96
97
98
99
100
D2 D4 D6 D8 D10 D12 D14 D16 D18 D20
RecognitionRate%
With Binarization WBSF

321
Table-5: FP verification rates for D20 using city-block distance
Threshold )(θ
Successful Decision FAR FRR
12.00 80.6896 0.0 19.3103
12.50 85.1724 0.0 14.8275
13.00 88.2758 0.0 11.7241
13.50 91.7241 0.0 8.2758
14.00 94.1379 0.0 5.8620
14.50 96.5517 0.0 3.4482
15.00 97.9310 0.0 2.0689
15.50 97.9310 0.0 2.0689
16.00 97.5862 0.3448 2.0689
16.50 97.5862 1.3793 1.0344
17.00 95.8620 3.1034 1.0344
17.50 95.1724 4.1379 0.6896
18.00 93.4482 5.8620 0.6896
18.50 92.0689 7.2413 0.6896
19.00 89.6551 9.6551 0.6896
The optimum threshold of Crossover Error Rate (CER) is the point where the False
Rejection Rate (FRR) and the False Acceptance Rate (FAR) curves meet in verifying user's
identity. The variation of FAR and FRR with different threshold values are also shown in Fig.
(13) and (14),where the obtained CER are approximately 4.03 and 16.45 for Euclidean and
city-block distances respectively.
Fig. (13): FAR and FRR Performance Curve for different threshold levels using
Euclidean distance
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
2.6 2.85 3.1 3.35 3.6 3.85 4.1 4.35 4.6 4.85 5.1 5.35 5.6
Threshold values for Euclidean distance
ErrorRate%
F A R F R R

322
Fig. (14): FAR and FRR Performance Curve for different threshold levels using city-
block distance
V. CONCLUSIONS
A fingerprint recognition system for 490 FP samples is presented that relies on wavelet
features. It is found that 4-level wavelet decomposition is appropriate for feature vector
construction where all the recognition rates are the highest among almost all Daubechies
functions. In the closed-set FP recognition, the obtained recognition rates are below 90% due to
the imperfections in the FP images. To enhance the recognition rates further, a number of
preprocessing operations are used prior to wavelet transform and more than 96% recognition
rates are achieved in some Daubechies functions.
The results have shown that the proposed WBSF method outperform the conventional
wavelet based recognition method. It seems that, the additionally selected bands provide extra
information and contribute in enhancing the recognition rates to attain 100% for D6, D8, ...,
D20 according to the test conditions considered in the work.
The open-set FP verification mode is also presented for 290 trials from 29 persons. The
obtained verification rates, greater than 97%, using WBSF method are quite acceptable.
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