Available online at www.sciencedirect.com
Available online at www.sciencedirect.com
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2021) 000–000
Procedia
Computer
Science
(2021)
000–000
Procedia
Computer
Science
19200
(2021)
642–649
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www.elsevier.com/locate/procedia
25th International Conference on Knowledge-Based and Intelligent Information & Engineering
25th International Conference on Knowledge-Based
Systems and Intelligent Information & Engineering
Systems
A
A Deep
Deep Learning
Learning Approach
Approach to
to Subject
Subject Identification
Identification Based
Based on
on
Walking
Patterns
Walking Patterns
Cezara Beneguiaa
Cezara Benegui
a Department of Computer Science, University of Bucharest, Romania (e-mail: cezara.benegui@fmi.unibuc.ro
a Department of Computer Science, University of Bucharest, Romania (e-mail: cezara.benegui@fmi.unibuc.ro
Abstract
Abstract
For the time being, smartphone devices rely on direct interaction from the users for unlocking and authentication purposes through
For
the time
being, smartphone
devices
rely on
direct
interactionorfrom
the users
for unlocking
and authentication
purposesauthentithrough
implicit
authentication
systems such
as PINs,
facial
recognition
fingerprint
scanning.
While different
passive two-factor
implicit
authentication
such
as PINs,
facial
recognition
or fingerprint
scanning.
While different
passivesystem.
two-factor
authentication systems
based onsystems
machine
learning
were
explored
in recent
work, all require
an implicit
authentication
In this
study,
cation
systems
based onand
machine
learning
wereauthentication
explored in recent
work,
all on
require
an implicit
system.
In this study,
the focus
is to develop
introduce
a passive
system
based
walking
patterns.authentication
In this scenario,
the authentication
the
focus
is to developauthenticates
and introduce
passive
authentication
based
walking
patterns.
thisofscenario,
the authentication
system
continuously
thea user
in the
background,system
without
any on
further
action.
To theInbest
our knowledge,
this is the
system
continuously
authenticates
theprocessed
user in thewith
background,
anybetter
further
action. Togait-based
the best ofmotion
our knowledge,
this is the
first study
in which the
data sets are
the aim towithout
generate
performing
signals. Compared
first
study instudied
which work,
the data
are processed
withstage
the aim
to generate
better tiny
performing
gait-based
motion
signals.signals.
Compared
to previous
wesets
employ
a processing
in which
we extract
frames of
data from
the motion
Our
to
previous studied
work, we
a processing
in learning
which we
tiny movement
frames of data
from the
Our
contribution
of processing
gaitemploy
data, allows
for morestage
robust
of extract
the subject
and lowers
themotion
numbersignals.
of samples
contribution
of
processing
gait
data,
allows
for
more
robust
learning
of
the
subject
movement
and
lowers
the
number
of
samples
required to classify a user thereafter. Hence, our approach is more robust compared to using raw gait signals. Further, we transform
required
classify a user
thereafter.
approach
is more
compared
to using
raw gaitthe
signals.
Further, the
we transform
them
intotogray-scale
images
for deepHence,
neuralour
network
training
androbust
feature
extraction.
Conducting
experiments,
empirical
them
gray-scalethat
images
for deep
neural
network
training
extraction.
the experiments,
the presented
empirical
resultsinto
demonstrate
subjects
can be
identified
with
a very and
highfeature
accuracy
through Conducting
walking patterns
employing the
results
demonstrate
that
subjects
can
be
identified
with
a
very
high
accuracy
through
walking
patterns
employing
the
presented
techniques. Empirical results outline that a system based on gait data can be utilized as a passive authentication system. Therefore,
techniques.
Empirical
results
outline
that aemploying
system based
on gait data
can be utilized
as a passive
authentication
Therefore,
it
is concluded
that deep
neural
networks
the technique
described
in this work
for gait-based
featuresystem.
representation
are
it
is concluded
that deep neural
networks employing
the technique
well
suited for continuous
and unobtrusive
authentication
systems. described in this work for gait-based feature representation are
well suited for continuous and unobtrusive authentication systems.
©
2021 The
The Authors.
Authors. Published
Published by
by Elsevier
Elsevier B.V.
B.V.
© 2021
©
2021an
The Authors.
Published
by Elsevier
B.V.
This
This is
is an open
open access
access article
article under
under the
the CC
CC BY-NC-ND
BY-NC-ND license
license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
(https://creativecommons.org/licenses/by-nc-nd/4.0)
This
is an open
access
article under
the
CC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review
under
responsibility
ofofthe
committee
ofof
the
KESInternational.
International.
Peer-review
under
responsibility
thescientific
scientific
committee
KES
Peer-review under responsibility of the scientific committee of the KES International.
Keywords: motion data; deep neural networks; user identification; continuous authentication
Keywords: motion data; deep neural networks; user identification; continuous authentication
1. Introduction
1. Introduction
The user identification task has been explored thoroughly recently. With the advancement of technology, more and
Thetechniques
user identification
task has
been
thoroughly
recently.
the advancement
of technology,
more
and
more
are introduced
with
theexplored
aim to solve
such tasks.
FromWith
standard
PINs, to unlocking
patterns and
facial
more
techniques
are
introduced
with
the
aim
to
solve
such
tasks.
From
standard
PINs,
to
unlocking
patterns
and
facial
recognition systems, user identification requires interaction with a device in all cases. Nowadays, a great variety of
recognition systems, user identification requires interaction with a device in all cases. Nowadays, a great variety of
E-mail address: cezara.benegui@fmi.unibuc.ro
E-mail address: cezara.benegui@fmi.unibuc.ro
1877-0509 © 2021 The Authors. Published by Elsevier B.V.
1877-0509
© 2021
Thearticle
Authors.
Published
by Elsevier B.V.
This
is an open
access
under
the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
1877-0509
© 2021
Thearticle
Authors.
Published
by Elsevier B.V.
This
is an open
access
under
the scientific
CC BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review
under
responsibility
of the
committee
oflicense
the
KES(https://creativecommons.org/licenses/by-nc-nd/4.0)
International.
This is an open
access
article under
the CC BY-NC-ND
Peer-review under responsibility of the scientific committee of the KES International.
Peer-review under responsibility of the scientific committee of KES International.
10.1016/j.procs.2021.08.066
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Cezara Benegui / Procedia Computer Science 00 (2021) 000–000
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components are included in smartphones: high performance cameras, magnetometers, proximity and motion sensors,
to name a few. Recent works[1, 2, 3, 4] proposed unobtrusive authentication systems that rely on motion sensors
to identify users through machine learning techniques, such as deep neural networks. Although such systems are
developed to improve implicit authentication systems, user interaction is not entirely eliminated.
In this work, we study new methods for continuous unobtrusive background authentication for smartphone devices,
based on walking patterns. Using gait data, a subject can be continuously registered and authenticated, without any
implicit interaction. This work explores a novel approach processing gait motion signals and utilizing the in the subject
identification task. To the best of our knowledge, we are the first who processed waling based motion signals into tiny
data frames and further transforming them into gray-scale images.
The goal of this work is to demonstrate that our technique of processing walking pattern data can attain substantial
results in the subject identification task through machine learning models. To accomplish our work, we follow the
same data set introduced by Vajdi et al. [5] in their work. The data set consists of motion sensor values and additional
meta information collected from 93 subjects that perform two walking sessions between two predefined points. Each
recording is performed using two iPhone 6s devices, placed on the left waist and the right thigh of the subject. During
each walking session, the motion sensors available on the device, namely accelerometer and gyroscope, yield data
points on three axis (X,Y,Z). In our experiments, we further process the data set into tiny frames of walking patterns,
with the aim to attain more robust signals.
Following the experimental settings described by Benegui et al. [1], the resulted images from our processed data
set, are fed as inputs to CNN models. Compared to their work that employes 150 samples of motion data collected
while performing taps on a smartphone device screen, our approach utilizes gait data which is represented by more
distinct features. Compared to a short tap gesture, the biomechanics of walking yield very different signals. The
resulted frames are transformed into gray-scale images. Furthermore, we extract embeddings from the CNN models
and pass them to SVM classifiers for the subject identification task. Therefore, each processed tiny frame of gait data
can be utilized to identify a subject. We present empirical results for each of the conducted experiments, that outline
the performance of subject identification.
The rest of this paper is organized as follows. Related work is discussed in Section 2. The methods utilized and the
techniques are described in Section 3. The gait-based user identification is detailed in Section 4. Finally, conclusions
are drawn in Section 5.
2. Related Work
2.1. Access control systems and protocols
Nowadays, traditional authentication systems available for smartphone devices such as PIN unlocking, fingerprint
scanning and face detection are prone to well known attacks [6, 7, 8, 9, 10, 11]. With smartphone usage increasing
on a global scale, novel authentication systems are required to protect data integrity. Authentication system based on
camera PRNU fingerprint [12, 13, 14] or on motion sensors [15, 3, 4, 16], were recently introduced with the aim to
offer continuous and unobtrusive user identification.
Furthermore, recent research show that such systems can be included in complex authentication protocols. However, one strategy alone might not provide high gains. Notwithstanding, literature shows that multiple techniques
combined yield large improvements. Zhongjie et al [12] introduced the ABC authentication protocol that is based on
smartphone camera fingerprint (PRNU). In addition to the traditional credentials exchange, the protocol takes advantage of passive identifiers in the process. Withal, the aforementioned protocol requires additional steps to be performed
by the user.
Smartphone devices embed a large number of sensors. By all of them, motion sensors are best candidates when it
comes to generation of large amounts of passive data. Additionally, natural walking patterns, called gait, are regarded
as a biometric traits [17, 18]. Therefore, authentication systems based on motion sensors do not require any implicit
action from the user, the data being collected in background. In comparison to multiple other data streams, such
information is not usually stored or shared in open contexts like social networks or shared folders. Hence, motion data
can be regarded as secure and private data streams, making them a suitable candidate for user identification tasks.
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2.2. User identification based on motion data
Recent studies have shown that user identification based on motion sensors [19, 5, 20, 21, 22], can attain high
accuracy rates, therefore, making those methods great candidates for continuous or two factor authentication systems.
Amongst literature, a broad variety of methods have been developed to identify users through different movement
patterns. Notable accuracy was attained using different techniques, starting from micro-movements recorded while
users perform a signature on a smartphone device [23], to eye movement identification systems [24, 25, 26], to
continuous recording of hand movement and user profiling based on accelerometer and gyroscope data [2].
Other studies rely on a combination of multiple sensors, including motion, magnetometer and pressure sensors [22].
While different work gather sensor information from simple movements captured during a smartphone use, gait or
more complex movement can also be used for subject identification. The best performing recent methods using motion
data are based on convolutional neural networks or recurrent neural networks [1, 19, 5]. One of the best performing
methods was introduced by Vajdi et al. [5], which obtains an accuracy of up to 99.10%.
3. Method
In this section, we describe our signal recording procedure alongside with the data processing technique. Further,
we review the deep learning model architectures and feature extraction process. For each network type, we describe
the architectures and the user identification process.
3.1. Signal Recording and Processing
To conduct our experiments, we employ a comprehensive gait database introduced by Vajdi et al [5]. The data
set is composed of accelerometer and gyroscope sensor values, collected from 93 subjects, utilizing two iPhone 6s
devices. Each subject carries one device on the right wrist and one on the left waist. The sensor values are collected at
a frequency of 100Hz while the subjects perform two walking sessions on distance of approximately 320 meters. For
the experiments conducted in this work, the values correlated to accelerometer and gyroscope are extracted from the
database and processed. Our contribution on the data set is described next.
While the biomechanics of walking yields complex data frames, our original approach to collect tiny frames generates more robust data points that puts an emphasis on distinctive biometric features of the subject. For each subject,
each of the 6 axis (3 axis for accelerometer and 3 axis for the gyroscope) are concatenated into a single data sequence.
Each axis is regarded as a 1D vector. With the aim to create more data points and demonstrate our novel approach
to gait pattern identification, the concatenated result is split into sub-sequences of 150 discrete values. The data is
collected at 100Hz, therefore, it results tiny sequences of walking data equal to time frames of 1.5 seconds. Lastly,
each data point of the resulted sequences is normalized and transformed such that its value is represented by a positive
integer between 0 and 255.
For training and evaluation of our method, we utilized data from a sub-set of 50 subjects. The remaining samples
representing the disjoint subset of 43 users, are utilized to simulate impersonation attacks.
We employ our CNN models within the identification task following the experimental settings described by
Benegui et al. [1]. We utilize the same de Brujin [27] sequence, with the aim to compose gray-scale images with
dimensions equal to 25 × 150 pixels.
3.2. Subject Classification Task
In order to perform the subject classification task, we employ a binary-classifier, namely an SVM [28]. Throughout
experiments, we implement the classifier utilizing two types of kernels: Radial Basis Function (RBF) and linear. In
the case of RBF, during optimisation phase, the model determines a hyperplane that discerns samples by a maximum
margin. To streamline our work and conduct experiments in a replicable approach, we employ an implementation
from Scikit-learn [29] for the SVM. Different values for regularization parameter C, namely 1, 10, 100, are selected
to experiment and obtain the best results in the pattern classification task. Therefore, after applying the SVM on the
data set, subjects are either classified as legitimate users, if the classifier predicts a positive label, or as an adversarial
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if the model predicts a negative label. Further, the CNN architectures described in the following section are employed
as feature extractors.
3.3. Deep learning models and feature extraction
Deep learning models are known as best performing systems in object recognition and computer vision tasks [30,
31, 32, 33, 34, 35]. In order to perform the experiments described further, two different deep neural network types are
employed, namely CNNs and LSTMs. Networks described forward are used in our experimental setting as embedding
extractors. The scope of this action is to generate and provide a robust inputs for the SVM.
3.3.1. CNN Architectures
In this paper, we propose four CNN architectures of different depths to be utilised as embedding extractors. In
general, all networks share a similar architecture, as follows: each network utilizes Softmax activation for the classification layer while Rectified Linear Unites (ReLU) [36] are utilized as the activation functions for all other layers.
The first CNN architecture is composed of a convolutional (conv) layer followed by 2 fully connected (fc) layers.
Each fc layer has 256 neurons and an applied dropout rate of 0.4. Lastly, the classification layer follows. Similarly,
the second architecture with 6-layers is composed of 3 conv layers, 2 fc layers and the Softmax layer.
Correspondingly, two more networks are utilised in the experiments consisting of 9 layers and respectively 12layers. The 9-layers CNN has 2 fc layers, 6 conv layers and the classification layer while the deepest network has 9
conv layers, 2 fc layers and a Softmax layer.
Training and evaluation of the models are performed using Adam optimizer with a categorical cross-entropy loss
function. While other methods converge slower, Kingma et al [37] demonstrated that the Adam optimizer is faster,
thus rendering it the best choice.
3.3.2. LSTM Architectures
While the gait data set is represented by time-series data, we find it applicable to employ ConvLSTM models in
the experiments. While ConvLSTM models can work directly with data, less pre-processing is required on the data
set. However, with the scope to keep the same number of samples as in the case of the CNN models, frames of data
with a size of 6 × 150 (6 rows, 150 data points) are created. Each row represents one sensor axis. Sequences of 150
values per axis are extracted to match the frame lengths described in the image generation process. The ConvLSTM
networks have an input of size 6 × 150. In comparison to the CNN models, only one LSTM architecture is selected
for the experiments performed, namely the 6-layer ConvLSTM.
The architecture of the 6-layers network is described next. The first convolutional layer of the network utilizes a
kernel size of 1 × 3, 64 filters and ReLU as an activation function. The next layer utilizes a higher number of filters,
namely 128, yet the same activation function and kernel size. The 3rd convolutional layer has the same structure as
the second, except the number of filters. Within the 3rd layer we utilize 256 filters. The following two layers are
represented by fc layers with 256 neurons and ReLU activations. Finally, as in the case of the CNN models, the
activation layer is represented by a Softmax layer, which contains 50 neurons, equal to the number of classes used in
experiments.
3.3.3. Embeddings Extraction
Both the ConvLSTM and CNN network types are exploited as feature extractors. After training, each network is
utilized as a prediction model. During each prediction, the values generated in the second to last layer of the network
are extracted as feature vectors. While each fully connected layer is composed of 256 neurons, the output will be
composed of 256 values. The feature vector extracted is stored and correlated to an input data sample. Further, the
resulted vectors are used as inputs for the SVM models, with the aim to perform the user classification task.
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4. Experiments
4.1. Data set
We employed the same gait data set introduced by Vajdi et al [5], composed of motion sensor data and additional
meta information collected from 93 subjects that performed two walking sessions. Each recording is performed using
two iPhone 6s devices, placed on the left waist and the right thigh of the subject. During each walking session, the
motion sensors on the device, accelerometer and gyroscope, produce values on three axis (X,Y,Z) at a sample rate of
100Hz.
For each user, the collected data from the two walking sessions, on a distance of approximately 320 meters, is
further processed. More robust data points that puts an emphasis on distinctive biometric features of the subject are
generated by collecting tiny frames of data from the walking session signals. The frames are equal to 1.5 seconds of
walking and consists of 6 axis (one for each sensor axis) with 150 values each. Our contribution of processing gait
data, allows for more robust learning of the subject movement and lowers the number of samples required to classify
a user thereafter. The resulted data set is randomly split into two disjoint set of users with lengths of 50, respectively
43 subjects. The two resulted sets are used for model training, validation and the test classification task.
The first subset of users, consisting of 50 subjects is utilized for user recognition experiments. In the conducted
experiments, we employ 400 data samples per user. For each user, we select 200 samples for training and validation,
namely 160 samples for training and 40 samples for validation based on a 80% − 20% split ratio. Furthermore, 200
samples are used for testing experiments, employing SVM classification. Nonetheless, the remaining subset of 43
users, is utilized for impersonation attack experiments.
4.2. Evaluation Metrics
To evaluate our technique, we employ classification accuracy as the main metric for evaluation. In the subject
classification task, we compute and track the accuracy of the models (ACC), false acceptance rate (FAR) and false
rejection rate (FRR). FAR represents the ratio between false acceptances and the sum of all negative samples while
false rejection rate is the ratio between all false rejection and the sum of all positive samples.
4.3. Parameter Tuning
The experiments conducted are based on the optimal hyper-parameters described by Benegui et al [1] in a similar
motion-based user identification task using convolutional neural networks. Therefore, a learning rate of 10−3 and a
batch size of 32 is selected. In regards to the training process, all models are trained for 50 epochs using Adam as the
optimizer function of choice [37].
4.4. Experiments structure
For each of the selected network types, training and validation is done using a 80% − 20% split ratio. Training of
each model is performed using 160 samples per user and validation for is done using the remaining 40 samples. By
eliminating the classification layer from both network types and extracting the output of the last fully connected layer,
a feature vector consisting of 256 values is obtained. The resulted feature vector is used as input for an SVM classifier,
with the aim to identify the subjects during the experiments.
4.5. Results
4.5.1. Subject Classification Based on Gait Motion Data
The empirical results obtained using different CNN architectures are described in Table 1. We can observe that,
while the depth of the architecture increases, both the validation accuracy and training accuracy tend to drop slightly
using a mini-batch size equal to 32. However, as Jastrzebski et al.[38] noted, the mini-batch size and the learning rate
are strongly correlated. Throughout experiments, a fixed learning rate equal to 10−3 was selected while the mini-batch
size was varied to take values equal to 32, 64 or 128. As seen in Table 1, employing a larger mini-batch size and
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Table 1. Train and validation accuracy rates of various depths CNN architectures, on the multi-class subject classification task. Each architecture is
trained with three different mini-batch sizes. Train and validation accuracy represent an indicator of the network extracted embeddings robustness.
The best results are highlighted in bold.
Model
6-layer CNN
9-layer CNN
12-layer CNN
Batch
size
32
64
128
32
64
128
32
64
128
Accuracy
Training Validation
99.12%
98.95%
99.25%
99.87%
99.37%
99.92%
98.77%
98.65%
99.53%
99.50%
98.53%
98.45%
97.73%
93.20%
99.06%
99.12%
99.64%
99.92%
a deeper network yields the best results, with a training accuracy of 99.64% and a validation accuracy of 99.92%.
While a mini-batches of 128 samples are optimal for 12-layers and 6-layers CNN, the 9-layers network attains better
results using 64 samples. Empirical results presented in Table 1 shows that the 6-layer and 12-layer CNN networks
have stronger generalization capacity, attaining a validation accuracy of 99.92%. Nonetheless, the shallower network
composed of 6 layers yields better generalisation results than the 12-layers network, when it comes to different batch
sizes. Hence, throughout the following experiments the 6-layers CNN based on mini-batches of 128 samples is utilized
for subsequent experiments.
Table 2. Train and validation accuracy rates of the 6-layer CNN architecture versus the 6-layer ConvLSTM, on the multi-class subject classification
task. All models are trained with mini-batches of 128 samples and produce 256-dimensional embeddings. Train and validation accuracy represent
an indicator of the network extracted embeddings robustness. The best results are highlighted in bold.
Model
6-layer ConvLSTM
6-layer CNN
Accuracy
Training Validation
97.91%
94.5%
99.37%
99.92%
Both the CNN and ConvLSTM networks are used as feature extractors within the experiments, thus, the ConvLSTM is trained in a similar fashion to the CNN networks. Table 2 shows a comparison between the best performing
CNN and the ConvLSTM. It can be observed that the ConvLSTM architecture does not surpass the accuracy and
generalization capacity of the 6-layers CNN. While the 6-layers ConvLSTM performs best, the validation accuracy
(94.5%) is 5.42% lower than the best performing CNN.
Table 3. Subject classification results employing SVM classifier based on CNN and ConvLSTM features. Two kernel functions, linear and RBF
are applied to the SVM along with the regularization parameter C of different values. The reported accuracy, FAR and FRR values, represent the
average test accuracy determined on the 50 subjects involved in the user identification task. The best results are highlighted in bold.
Model
LSTM + SVM
CNN + SVM
Kernel
linear
RBF
linear
RBF
C
1
10
1
10
Accuracy
98.67%
98.79%
98.67%
98.79%
FAR
1.94%
1.04%
1.94%
1.04%
FRR
0.72%
1.38%
0.72%
1.38%
After embeddings extraction from both networks, an SVM classifier is applied. Table 3 highlights the empirical
results obtained by employing an SVM classifier on the extracted embeddings. Results show that both CNN and
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ConvLSTM extracted features yield equal results. While using different kernels and regularization parameters, the
accuracy variance between models is slim to none (0.12%). It can be noted that, by employing an RBF kernel, better
results are obtained. However, the false rejection rate increases when compared to a linear kernel.
5. Conclusion
In this paper we studied the subject identification task based on gait data. Implementing pre-trained CNN and
LSTM networks as feature extractors and SVM as classifiers produces great results in subject identification task. The
experiments show that gait-based data sets are very well suited for this task, attaining an accuracy rate of up to 98.79%.
Passive collection of gait data and continuous user identification is a strong two-factor authentication layer.
We hereby conclude that deep neural networks for gait-based subject identification are very promising, attaining an
accuracy up to 98.79%. Compared to [5], the presented method performs similarly, the difference being an accuracy
decrease of 0.31%. We thus conclude that our gait-based user identification system is suitable for industry usage,
having a high accuracy and a low misclassification rate. Furthermore, our system does not require any user interaction,
therefore rendering it a great unobtrusive security layer.
In future work, we aim to identify other solutions to further reduce the FAR and the FRR values, since we believe
that the ABC protocol still has enough potential to become a reliable authentication protocol.
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