Fast Fingerprint Classification with Deep Neural Networks
Daniel Michelsanti, Andreea-Daniela Ene, Yanis Guichi, Rares Stef, Kamal Nasrollahi
and Thomas B. Moeslund
Visual Analysis of People (VAP) Laboratory, Aalborg University, Aalborg, Denmark
Keywords:
Fingerprint Classification, Transfer Learning, Convolutional Neural Networks.
Abstract:
Reducing the number of comparisons in automated fingerprint identification systems is essential when dealing
with a large database. Fingerprint classification allows to achieve this goal by dividing fingerprints into several
categories, but it presents still some challenges due to the large intra-class variations and the small inter-class
variations. The vast majority of the previous methods uses global characteristics, in particular the orientation
image, as features of a classifier. This makes the feature extraction stage highly dependent on preprocessing
techniques and usually computationally expensive. In this work we evaluate the performance of two pre-
trained convolutional neural networks fine-tuned on the NIST SD4 benchmark database. The obtained results
show that this approach is comparable with other results in the literature, with the advantage of a fast feature
extraction stage.
1 INTRODUCTION
Physiological and behavioural characteristics have al-
ways been used to identify an individual. When re-
ferring to this identification approach, the focus is
on biometrics, which includes traits such as finger-
print, face, and voice. Lately, because of the progress
in computer processing, automatic biometric systems,
based on concepts developed long ago, have become
available (Mayhew, 2015).
Today, biometrics are generally preferred to the
traditional token-based systems, such as identity
cards, driver’s licences, keys, or knowledge-based
systems, such as passwords. The reason is that the
latter identification methods might be easily forgot-
ten or shared, which has a negative impact on their
reliability. The recent increase of fingerprint sensors
adopted in smartphones for login and payment secu-
rity systems, is only one example of this trend.
When a person requires to be identified through
his/her fingerprint, it is necessary to compare that trait
with the entire set of fingerprints in a database (Mal-
toni et al., 2009). If the database is large, this ap-
proach may be problematic for real-time applications
due to the high number of comparisons needed. For
this reason, fingerprints are often divided into differ-
ent classes. Generally, four classes are used: Arch
(A), Left loop (L), Right loop (R), Whorl (W). Some-
times the first class is divided into two categories,
Arch (A) and Tented arch (T), as shown in Figure 1.
In this paper, the four-class classification problem is
taken into consideration.
The existing methods for fingerprint classification
are based on classifiers that use hand-crafted features
extracted from fingerprint images (Maltoni et al.,
2009). These classification systems are highly depen-
dent on a preprocessing phase that increases the pro-
cessing time at test stage. Motivatedby the recent suc-
cess of deep learning techniques in many computer
vision tasks, the proposed system in this paper con-
sists of a pre-trained Convolutional Neural Network
(CNN) fine-tuned on a subset of the NIST SD4 bench-
mark database (Watson and Wilson, 1992a). The
two used CNN architectures are VGG-F and VGG-S
(Chatfield et al., 2014). The main contribution of this
work is to show the performance of transfer learning
for a problem which is generally tackled by extracting
visually meaningful features.
The paper is structured as follows: in Section 2
the state-of-the art is detailed. Section 3 provides a
description of the database used in this work, and Sec-
tion 4 shows the architecture of the adopted networks,
the parameters for training, and the evaluation proto-
col. Finally, the results are presented and discussed,
and the conclusions are drawn.
202
Michelsanti D., Ene A., Guichi Y., Stef R., Nasrollahi K. and Moeslund T.
Fast Fingerprint Classification with Deep Neural Networks.
DOI: 10.5220/0006116502020209
In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pages 202-209
ISBN: 978-989-758-226-4
Copyright
c
 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Arch (A) Tented arch (T) Left loop (L)
Right loop (R) Whorl (W)
Figure 1: Fingerprint classes.
2 PREVIOUS WORK
2.1 Fingerprint Classification
Fingerprint classification is a pattern recognition
problem that has received considerable attention for
its difficulty, due to the small inter-class variability
and the large intra-class variability (Maltoni et al.,
2009). A list of the most relevant works can be found
in (Galar et al., 2015).
When referring to fingerprints, images show three
kinds of features based on the level of ridge details
(Maltoni et al., 2009):
• At the first level (global) we have three possible
singular regions: loop, delta, and whorl.
• The second level (local) allows us to find small
details in fingerprint patterns, known as minutiae.
• The third level (fine-detail) shows all the attributes
of the ridges.
Although some approaches that use feature-learning
algorithms have been proposed in the past, such as
(Tan et al., 2003), global characteristics are generally
used for classification, in particular: ridge line flow,
orientation image, singular points, and Gabor filter
responses (Maltoni et al., 2009). Among them, the
orientation image is the most used.
The feature extraction can be performed in several
ways, depending on the kind of feature, the quality of
the image, and the accuracy that the following classi-
fication phase requires. The goal of the classification
stage is to learn a classifier based on labelled finger-
prints. The techniques proposed in the literature are
evaluated on a number of different databases (Galar
et al., 2015), like NIST SD4, NIST SD14 (Watson
and Wilson, 1993), NIST SD9 (Watson and Wilson,
1992b), with the first one as the most popular one.
They can be divided into these categories (Maltoni
et al., 2009):
• Rule-based - The classification is made according
to the number of singularities:
– Arch has no singular points.
– Tented arch, and left/right loop have one loop
and one delta.
– Whorl has two loops or a whorl, and two deltas.
• Syntactic - These methods are based on a gram-
Fast Fingerprint Classification with Deep Neural Networks
203

mar defined by symbols extracted from the fea-
tures of the fingerprints.
• Structural - It includes methods that build data
structures, such as trees or graphs, for a better
relational organisation of low-level features into
higher-level hierarchical structures.
• Statistical - In this case, the classifier is a statisti-
cal one, like Bayesian Decision Rule, K-Nearest
Neighbour, and Support Vector Machine (SVM).
• Neural network-based - Generally the classifier
consists of a Multi Layer Perceptron, after the di-
mensionality reduction performed on the feature
vector.
• Multi-classifier - This category includes all the ap-
proaches that combine two or more classifiers.
Our work differs from the previous ones because it
is not based on global features, but it performs the fea-
ture extraction and the classification through a CNN.
2.2 Transfer Learning
The main problem of using CNNs is the long time and
the huge size of the dataset needed for training. A
solution is to use a network pre-trained on a different
dataset. There are two possibilities:
1. Use the network to extract features and then train
a linear classifier, such as a Support Vector Ma-
chine (SVM).
2. Fine-tune the network, by re-training some or all
the layers.
(Razavian et al., 2014) showed the first possibility
applied to images of different domains. Using fea-
tures extracted from the OverFeat network (Sermanet
et al., 2013) pre-trained on the ImageNet database,
they analysed how the results can be used for several
computer vision recognition tasks. Experimenting
with visual classification (image classification, fine-
grained recognition, attribute detection), they have
obtained some excellent results. In particular, they
showed the difference between CNN combined with
SVM, either with and without data augmentation for
all the experiments, and they compared with several
methods from the previous state-of-the-art, obtaining
a substantial improvement.
Also (Donahue et al., 2014) highlighted the use
of CNNs for feature extraction. They used the neural
network architecture of (Krizhevsky et al., 2012), and
tested it on several recognition tasks, such as scene
and subcategory recognition. In both cases the results
were better than the ones of the state-of-the-art.
Similarly to the previously mentioned studies,
(Hertel et al., 2015) addressed the drawback of the
time needed to train a deep CNN. In order to ac-
complish this, they first trained a deep CNN on the
ILSVRC-12 large dataset from an ImageNet compe-
tition, and fine-tuned it on three datasets: MNIST
(LeCun et al., 1998), CIFAR-10 and CIFAR-100
(Krizhevsky and Hinton, 2009). The MNIST dataset
contains grayscale images of handwritten digits while
CIFAR-10 and CIFAR-100 contain small color im-
ages of natural objects. By maintaining the learned
convolution kernels and retraining only the classifica-
tion part on different datasets, they obtained an accu-
racy rate comparable to a full training approach, sug-
gesting that CNNs are able to learn generic feature
extractors that can be used for different tasks.
(Nogueira et al., 2016) used fingerprint liveness
detection datasets to fine-tune two different CNNs:
CNN-Alexnet (similar to AlexNet (Krizhevsky et al.,
2012)), and CNN-VGG (Simonyan and Zisserman,
2014). The obtained results show that pre-trained
CNNs can detect whether a fingerprint is false or
real with state-of-the-art performance, even though
no task-specific techniques are used. They also re-
ported that training a classifier with multiple datasets
improves accuracy and robustness. This, in turn, sug-
gests that combining different datasets can avoid fine
tuning of hyper-parameters. For our work, the main
contribution of this study is that pre-trained networks
on natural images can be successfully used in finger-
print domain.
3 DATABASE
In this work the NIST Special Database 4 (NIST SD4)
has been used for the experiments. It is the most
important benchmark for the fingerprint classification
problem. It is a dataset of 4.000 8-bit grayscale fin-
gerprint images provided by the National Institute of
Standards and Technology and collected by (Watson
and Wilson, 1992a). It contains 512×512 images,
classified in five classes, Arch (A), Tented arch (T),
Left loop (L), Right loop (L), and Whorl (W), with
400 image pairs per class. The two images of each
pair are two different rollings of the same finger.
4 METHODOLOGY
The pre-trained models that have been used are VGG-
F and VGG-S, and their architecture is shown in Table
1. They were trained on ILSVRC-2012 using gradi-
ent descent with momentum (Chatfield et al., 2014).
VGG-F has a fast processing, but its accuracy in many
tasks is outperformed by the VGG-S slow architecture
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(Chatfield et al., 2014). We use a 224×224 finger-
print image, with the average fingerprint image of the
training set subtracted, as input of the two networks.
Both of them have 19 layers, with five convolutional
and three max-pooling ones. A rectified linear unit
(ReLU) is used as a non linear activation function. At
the end, a softmax layer is used to obtain a probability
distribution, used as confidence values of the classi-
fier.
The fine-tuning of the CNNs was performed af-
ter the replacement of the last fully connected layer,
with one that has a four-element vector, a dimension-
ality which is equal to the number of classes of our
problem. The weights of this layer were initialised
from a Gaussian distribution. The learning followed
a procedure similar to other works in the literature,
like (Chatfield et al., 2014), with the adoption of gra-
dient descent with momentum. The networks were
re-training for 140 epochs, where the images of the
training set were previously flipped. Since fingerprint
images are not flip invariant, in case of right and left
loops we changed the label of the images accordingly,
so a right-loop fingerprint was assigned to the left-
loop class when flipped and viceversa. No change
of class is required for whorls and arches. In this
way, the number of input images was doubled. Dur-
ing training some data augmentation techniques were
adopted (Figure 2). In particular the images were
randomly rotated between -20
◦
and +20
◦
, stretched
and/or shrunk horizontally and vertically, and modi-
fied by the application of a Gaussian noise and a gray
level mapping. After the augmentation, the 512×512
images were cropped in the central 400×400 part and
resized in order to have the suitable dimensions for
the input of the network.
During the fine-tuning, the weights of a layer are
updated using a local learning rate multiplied by the
global learning rate (shared with all the layers). We
set the local learning rates for the earlier layers, which
learn more generic features, close to 0, and the ini-
tial global learning rate to 0.05. Then, we decreased
the learning rate to 0.02 after 10 epochs, and again to
0.01 after 20 epochs. Later we reduced the learning
rate by a factor of 10 every time that the validation
error stop decreasing. In total, five different learning
rates were used. Moreover we set a momentum value
to 0.9, a weight decay value to 0.0001, and a batch
size value to 100. In order to accelerate the training
we added batch normalisation layers. As shown by
(Ioffe and Szegedy, 2015), this approach allows to use
higher learning rates and in some cases it acts as a reg-
ulariser. The reason why batch normalisation helps is
that inside a deep network the weights adjust the data
that flows through it so that some values may differ
Figure 2: Data augmentation techniques. On the top the
original image is shown. On the bottom we have the same
image after the cropping of the central area and the applica-
tion of: a rotation (top-left); a stretching and shrinking (top-
right); a gaussian noise (bottom-left); a grey level mapping
(bottom-right).
a lot from each other. This issue, known as internal
covariate shift, is solved by normalising the data after
each convolutional layer. We also added two dropout
layers (Srivastava et al., 2014) to reduce overfit. The
idea in this case is to inhibit some nodes of the net-
work with a probability p (equals to 0.5 in our case)
during training.
As mentioned before, the database we used is
NIST SD4. Knowing that fingerprints are not evenly
distributed among the classes, only less than 10% of
them belong to arch and tented arch, and that dis-
Fast Fingerprint Classification with Deep Neural Networks
205

Table 1: VGG-F and VGG-S architectures.
VGG-F
layer 0 1 2 3 4 5 6 7 8 9
type input conv relu pool conv relu pool conv relu conv
support - 11 1 3 5 1 3 3 1 3
filt dim - 3 - - 64 - - 256 - 256
num filts - 64 - - 256 - - 256 - 256
stride - 4 1 2 1 1 2 1 1 1
pad - 0 0 0×1×0×1 2 0 0 1 0 1
layer 10 11 12 13 14 15 16 17 18 19
type relu conv relu pool conv relu conv relu conv softMax
support 1 3 1 3 6 1 1 1 1 -
filt dim - 256 - - 256 - 4096 - 4096 -
num filts - 256 - - 4096 - 4096 - 1000 -
stride 1 1 1 2 1 1 1 1 1 1
pad 0 1 0 0 0 0 0 0 0 0
VGG-S
layer 0 1 2 3 4 5 6 7 8 9
type input conv relu pool conv relu pool conv relu conv
support - 7 1 3 5 1 2 3 1 3
filt dim - 3 - - 96 - - 256 - 512
num filts - 96 - - 256 - - 512 - 512
stride - 2 1 2 1 1 2 1 1 1
pad - 0 0 0×2×0×2 0 0 0×1×0×1 1 0 1
layer 10 11 12 13 14 15 16 17 18 19
type relu conv relu pool conv relu conv relu conv softMax
support 1 3 1 3 6 1 1 1 1 -
filt dim - 512 - - 512 - 4096 - 4096 -
num filts - 512 - - 4096 - 4096 - 1000 -
stride 1 1 1 3 1 1 1 1 1 1
pad 0 1 0 0×1×0×1 0 0 0 0 0 0
tinguishing between these two classes is difficult be-
cause of their similarity, we chose to merge them into
one category, as done in other works from the litera-
ture. Therefore, we have a dataset of four categories
(arch, left loop, right loop, and whorl).
For the evaluation of the performance, we used
a protocol that can be considered standard, since it
is used in almost all the researches. The database,
containing 4.000 fingerprint images numbered from
f0001 to f2000 and from s0001 to s2000, was split
into two sets: training set (from f0001 to f1000 and
s0001 to s1000), and test set. Since 17.5% of the
fingerprints are considered ambiguous, they have two
classes assigned. For training, only one label is used,
while for testing all the two are. We also decided to
split the training set, choosing 100 different finger-
prints as our validation set.
5 RESULTS AND DISCUSSION
For the experiments we used MATLAB and MatCon-
vNet (Vedaldi and Lenc, 2015) combined with the
NVIDIA CuDNN libraries. The models have been
fine-tuned on the NVIDIA GTX 950M GPU.
Using the data augmentation techniques and the
parameters described in the previous section, we fine-
tuned the networks for 140 epochs, and in both cases
a training error of around 8% has been reached, as
shown in Figure 3 for VGG-S. The whole fine-tuning
process took around 9 hour for VGG-F and around 30
hours for VGG-S on our machine, which is an Asus
K550JX.
VGG-F allows us to achieve a 94.4% of accuracy,
with a testing time of 39 ms per image. The confusion
matrix is shown in Table 2, and it can be seen that the
network has problems in classifying right-loop and
left-loop fingerprints, with a misclassification rate of
11.25% and 7.33% respectively.
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206

0 20 40 60 80 100 120 140
Epochs
0
0.2
0.4
0.6
0.8
1
Error Rate
Figure 3: Fine tuning of the VGG-S network over 140
epochs.
Table 2: Confusion matrix when using VGG-F.
Assigned class
True class A L R W Accuracy
A 794 13 10 0 97.18%
L 23 354 3 2 92.67%
R 41 1 355 3 88.75%
W 3 5 8 385 96.01%
Table 3: Confusion matrix when using VGG-S.
Assigned class
True class A L R W Accuracy
A 809 8 7 0 98.18%
L 27 350 2 2 91.86%
R 31 0 357 6 90.61%
W 3 5 8 385 96.01%
On the other hand, using VGG-S a 95.05% of ac-
curacy was achieved, with a testing time of 77 ms per
image. Also in this case the network shows problems
in the classification of right-loop and left-loop finger-
prints, where the misclassification rate is 9.39% and
8.14% respectively (Table 3). Some examples of mis-
classified fingerprints can be seen in Figure 4.
Sometimes, the differences between two classes
are hard to determine, even for a human expert. For
this reason a rejection option can be applied: if a fin-
gerprint is hard to classify by the system, then it is re-
jected, and it will be evaluated by an expert. We chose
to apply a threshold to the confidence score of the best
class chosen by our classifier: if the score is less than
this threshold, then the fingerprint is rejected. Figure
5 shows the improvementsof the accuracyof our clas-
sifier based on VGG-S if a rejection option is applied.
Table 4 compares our results with similar works
Figure 4: Misclassified samples in NIST SD4 using VGG-
S. Top-left: arch classified as left loop. Top-right: arch clas-
sified as right loop. Bottom-left: left loop classified as arch.
Bottom-right: right loop classified as arch.
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
Rejection
0.95
0.96
0.97
0.98
0.99
Accuracy
Figure 5: Accuracy versus rejection curve for our classifier
based on VGG-S.
in the literature. We reported also the work of (Can-
dela et al., 1995) because it is considered a milestone
(Maltoni et al., 2009), since the introduced system,
PCASYS, is open source and it was one of the first
studies whose results were reported on publicly avail-
able databases. We can see that the achieved error
rate makes our method competitive, even though more
work should be done to get closer to the performance
reported by (Cao et al., 2013). However, the main ad-
vantage of our technique is that it is not based on any
of the standard features for classification, such as ori-
entation image (Cappelli et al., 1999), (Cappelli et al.,
2003), (Cappelli and Maio, 2004), (Park and Park,
Fast Fingerprint Classification with Deep Neural Networks
207

2005), (Tan et al., 2005), singular point (Li et al.,
2008), and ridge line flow (Candela et al., 1995). This
means that, apart from a crop of the image, no prepro-
cessing is needed at test time, keeping the computa-
tional complexity of our method low. As mentioned
before, we are able to classify a fingerprint image in
39 ms or 77 ms using VGG-F or VGG-S respectively.
This confirms that feature-learning approaches, like
the one of (Tan et al., 2005) that reported an aver-
age run-time for one fingerprint test of 71 ms on a
SUN Ultra II workstation with a 200MHZ CPU, are
faster. For example, (Cao et al., 2013) used an al-
gorithm where they classify the fingerprints based on
their orientation image. They obtain an average orien-
tation extraction time of 880 ms and an average clas-
sification time of 3.43 s on a 3.4 GHz Intel Pentium 4
processor.
Table 4: Error rates of different classification methods on
NIST SD4.
Method 4 classes
(Candela et al., 1995) 11.4%
(Cappelli et al., 1999) 5.5%
(Cappelli et al., 2003) 3.7%
(Cappelli and Maio, 2004) 4.7%
(Zhang and Yan, 2004) 7.5%
(Park and Park, 2005) 6.0%
(Tan et al., 2005) 6.7%
(Li et al., 2008) 5.0%
(Cao et al., 2013) 2.8%
Our Method - VGG-F 5.6%
Our Method - VGG-S 4.95%
6 CONCLUSION
In this paper, two pre-trained CNNs, VGG-F and
VGG-S, have been used to address the fingerprint
classification problem. The results show that the per-
formance obtained with our approach are close to
the state-of-the-art, with an accuracy rate of 94.4%
and 95.05% when using VGG-F and VGG-S respec-
tively. This confirms that transfer learning can be used
to achieve high accuracy in fingerprint classification.
The main advantage of our approach is that it does not
require a heavy preprocessing stage, as in the other re-
lated works, where some features, such as the orien-
tation image, have to be extracted. In our case, when
a fingerprint image is provided to the trained CNN, it
extracts a set of features with the filters learned during
the training, and classifies it.
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