PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR

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Abstract — This study attempts to build a statistical model to

identify a user, based on how the user picks up the phone and how

he/she holds the phone to the ear. Phone-pickup and phone-

holding methods are quantified using accelerometer readings on

the three axes. This study is based on the theory that every person

has a unique way of handling the phone during a phone call. The

minute variations in the angle of tilt and the micro movements

made during the phone call can be quite different between two

individuals. The Accelerometer gives a convenient method of

quantifying the phone movements and the change in orientation of

the phone on a three dimensional space. This study involves data

gathered from seven participants who made five test phone calls

each lasting for a little over 5 seconds. The study produced a model

that can predict the user with high accuracy using a smartphone

accelerometer.

Index Terms— Accelerometer, Biometrics, Authentication,

Weka.

I. INTRODUCTION

CCLEROMETER is a device that can detect the static

acceleration caused by the pull of gravity and the dynamic

acceleration caused by displacement or vibration of the device

[7]. The static acceleration gives useful insights into the

orientation of the device at different phases of the experiment.

The dynamic acceleration quantifies customer movements

while picking up the phone and gives insights into the micro

movements during the call. Most smartphones available in the

market today, have three-axis accelerometer included.

Biometric authentication methods often provide secure

alternatives to traditional authentication methods. In fact

several biometric authentication methods can be used in tandem

to build a secure and an easy-to-use system that provide

continuous authentication capabilities.

This paper describes a method of accelerometer-based

authentication that can be combined along with other means of

authentication to improve the accuracy of biometric-based

authentication systems.

A. SMARTPHONE ACCELEROMETER

Fig-1 shows the working principle of a smartphone

accelerometer. Seismic mass is fixed on the housing of the

accelerometer unit that is attached to the phone circuit. The

gravitational pull or user movements will change the orientation

This study is conducted as part of Capstone Program for the completion of

Masters Degree in Information Systems at Pace University during the Fall Semester of 2014.

of the seismic mass with in the sensor, generating changes in

the capacitance [27].

Fig. 1. Smartphone Accelerometer – picture credit Techulator.com[27]

B. WEKA

Weka [12] is a product of University of Waikato. It is among

the leading open-source tools used for building Data Mining

Systems. It is released under GNU General Public License

(GPL).

Weka requires data in a specific format called Attribute-

Relation File Format (ARFF). It provides a convenient utility

for converting Comma-Separated Values (CSV) format to

ARFF format. Weka allows preprocessing of the data before

applying the classifier algorithms to build the model. Weka also

supports a number of attribute evaluator algorithms to be used

for selecting or ranking attributes in the dataset. The evaluator

can be used for filtering noisy attributes or ranking the attributes

based on their relevance.

Weka supports a large collection of classifier algorithms to

be used for building statistical models in different contexts. In

this study Weka classifiers are used for achieving two things;

for finding a threshold value for splitting the data sessions into

three phases – picking-up, on-ear and placing-down. More

importantly, Weka is used for building the statistical models for

the four experiments conducted in this study.

After evaluating many of the available classifier algorithms,

the highest performance is observed for a particular classifier

algorithm called Multilayer Perceptron. This is a classifier

Biometric User Authentication on Smartphone

Accelerometer Sensor Data

Noufal Kunnathu,

Seidenberg School of CSIS, Pace University

A

PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR

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based or Artificial Neural Network (ANN) model. This

algorithm is discussed further in the next section.

C. Multilayer Perceptron Algorithm

Multilayer Perceptron is a feed-forward artificial neural

network model. It maps a set of input data to a set onto a set of

appropriate outputs [8].

Fig. 2. Multilayer Perceptron Model.

As shown in Fig. 2, MLP model contains three layers with

each layer fully connected to the others – the input layer, the

hidden layer and the output layer.

II. LITERATURE REVIEW

Sensor-based, biometric analysis is a relatively recent field

of research. There is an increased interest in this area due to the

proliferation of mobile devices and the wide availability of

sensors. There were many early studies that used

accelerometer-based biometric analysis for gait recognition

[2][10][14][22]. The studies produced promising results in

identifying user gait based on accelerometer devices worn by

the users. There is an interesting study that proposed

authentication via electronic pets [4][26] that imprints the user

characteristics and continuously nurture the characteristics to be

used as an authoritative token for securely identifying a user. A

recent study focuses on predicting user traits such as height,

weight, sex etc. from the data collected with the accelerometer

sensor with the volunteers taking short walks carrying an

Android phone [29]. There have been interesting studies

proposing gesture-based user authentication using

accelerometer sensor [20][11].

There have been researches in mining complex activities and

performing activity recognition based on the data collected

from the Accelerometer [23][21][19]. There are studies for

building Accelerometer-based gestural text-entry systems [29].

Accelerometer and gyroscope is combined in many

experiments. The combined data can be used for motion gesture

recognition using classifiers with dimensionality constraints

[18]. There is a recent study investigating mobile device

picking-up motion [9]. Also, there is a study conducted to

transparently identify the user of a smartphone when he/she is

on a phone call [5]. These last two references are very relevant

to the scope of this study. Those studies and this paper look at

the same problem but in different perspectives.

III. EXPERIMENT DESCRIPTION

The aim of this study is to identify a user from the

accelerometer readings. For the purpose of this study, data was

collected from the accelerometer from volunteers who agreed

to participate in the study. There are four experiments

conducted in this study. All four experiments are based on

different aspects of the same dataset. The first two experiments

investigate the phone holding-to-ear pattern. The third

experiment investigates the phone picking up activity and the

last experiment combines all the features to derive a high-

performing continuous authentication system.

A. Data Collection

The experiments required data corresponding to the phone

picking up action and the data for phone holding-to-ear activity.

Seven volunteers were selected for the data collection. The

participants included five males and one female with ages

ranging from 25 to 40, and a thirteen-year-old male. Each

participant is requested to repeat the following steps five times,

resulting in a total of 35 data samples.

Pick up the phone from a table

Hold it up to the ear for approximately 5 seconds

Place the phone back on the table

The Accelerometer data is recorded continuously while the

above steps are executed. Users are asked to hold the phone

naturally as if making a phone call. It is observed that 5 of the

7 users held the phone to their left ear, one to the right ear, and

one to both ears - sometimes the left ear and sometimes the right

ear. The selection of the ear resulted in variation of the

accelerometer readings but it did not appear to be a significant

factor in identifying the user.

The accelerometer data gives a unique perspective on the

way phone moves on all three axes while the user makes the

phone call. Each experiment session lasted a little over 5

seconds. Sampling rate of 25Hz is used for the data collection.

A data vector contains following attributes.

Time

Session-id

Ear-used

X-acceleration

Y-acceleration

Z-acceleration and

User-id

The Session-id, Ear-used and User-id are manually recorded

and added to the dataset. Other attributes are directly extracted

from the smartphone accelerometer sensor.

B. System Design

Android operating system was chosen for conducting the

study and a Nexus 5 device was used for all data collection.

An android application called “Sensor Kinetics Pro” [24] is

used for recording the accelerometer data. This app can record

readings from many sensors including accelerometer,

gyroscope, magnetometer, etc. However, the scope of this study

is limited to the accelerometer. So, the readings from other

sensors were not processed.

PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR

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Fig. 1 is a screenshot from the app that shows the readings

for Accelerometer on X, Y and Z axes.

Fig. 1. Screenshot from Sensor Kinetic Pro App.

Weka is used to analyze the sensor data. A number of other

tools were evaluated as part of the study. Weka was selected for

its intuitiveness, ease of use, and the rich feature set.

C. Data Pre-processing

As stated earlier, there are three phases for each experiment

session – picking phone up, holding up to the ear and placing

the phone down. Initial pre-processing task is to tag the data to

separate the data vectors for these three stages. This can be done

by plotting each session in Excel and visually inspecting the

graph. For example, see fig-2 for the line chart of a typical

session.

Fig. 2. Line Diagram for a typical experiment session.

Large variances can be observed at the beginning and end of

each session with relatively stable values for the middle part of

the experiment. The beginning part with large variances

corresponds to the “picking up” phase, the middle part

corresponds to the “on-ear” phase and the end part with

variances corresponds to the “placing down” phase. There can

also be 0 values very early and end of each session. These zero

values will be trimmed out as these correspond to the stationary

phone on the table.

In this study, a semi-automatic procedure is used for tagging

the data into stages. The first part is to build a model to correctly

tag the three stages. For this purpose, a movement rate metric is

created based on the rate of movement on three axes.

The training data set is built manually for a few

representative sessions. The training data and the computed

value for metric were loaded into Weka. J48 decision tree is

used to build a simple model to derive a threshold value for the

movement metric for the stages. The classifier returned a

threshold value for the movement as 0.776. Any vectors that

have a lower than the threshold value is tagged as “on-ear”

stage. The rest of the data is split into either “picking up” or

“placing down” based on the time of recording.

After the automated tagging is applied, the data is manually

inspected to make sure that the three phases are continuous. For

example, any vector tagged as “picking up” in the middle of an

“on-ear” phase is manually overridden to “on-ear”.

D. Experiments

Four experiments were conducted to build statistical models

for identifying users based on the Accelerometer data

associated with the phone call sessions.

1) EXPERIMENT-1: PHONE ON-EAR.

This experiment uses the average values for the X, Y and Z

readings from the accelerometer sensor. The aim is to build a

statistical model that can correctly predict the user from the

average values. The model will be built with the help of a

classifier algorithm available in Weka.

For this experiment, only the data vectors tagged as “on-ear”

is used. The average values are computed for X, Y and Z

readings for each session. The averages were computed by

building a pivot table in Microsoft Excel.

We are left with 35 feature vectors with following attributes.

Session-id

Ear-used

Average-X-acceleration

Average-Y- acceleration

Average-Z-acceleration and

User-id

In order to select the optimum attributes, an Attribute

evaluator called Chi-squared Attribute Evaluator [25] was used.

The evaluator ranked the attributes as shown in Table-1

Table 1. Attribute Ranking

It appears that averages on Z and X axes have higher

significance over other attributes in predicting the user id. The

attributes that have low significance can be eliminated one-by-

one as long as the removal doesn’t reduce the performance of

the system.

-15

-10

-5

0

5

10

15

time

0.313

0.63

0.948

1.265

1.582

1.9

2.216

2.533

2.851

3.316

3.862

4.406

4.953

5.498

5.993

6.538

7.083

7.628

X_value Y_value Z_value

PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR

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Figure-3 shows the clusters of instances when the values for

the two most significant attributes are plotted on X and Y-axes.

Fig. 3. Data visualization – Average Z value on horizontal and X value on

vertical axes

Each point in the graph represents a feature subset containing

average values for Z and X accelerometer readings. Different

colors are used for the feature vectors corresponding to distinct

user. The rectangles show the clustering. As observed from the

diagram, there is minimal overlap between the rectangles. Only

one user has the data spread-out across the graph. This user is

the one who used both ears for the experiments.

As discussed earlier, in this study Weka is used with the

Multilayer perceptron algorithm as the primary classifier for

building the model. The entire dataset was used as both training

set and the test set with a 35-fold cross-validation. Since there

were 35 sessions altogether, the 35-fold cross-validation will

test each session individually with the remaining 34 sessions

used for training.

Table-2 shows the results from the classifier. Out of the 35

sessions, 28 were correctly classified with an 80% success rate.

Table 2. Classifier Results Summary – Experiment-1

Kappa statistic [28] with a value 0.7667 shows a moderate

agreement with in the experiment data. Root-mean-squared-

error, root-absolute-error and root-relative-squared-error

provide metrics for comparing the rate of error in making the

correct prediction. These metrics can be used for comparison

between the experiments described in this paper in terms of

probability of errors with in the models.

Table-3 shows the confusion matrix that helps to visualize

how the measurement from each user was classified. The

diagonal shows the matches and the numbers marked outside of

the diagonal are the “confusion”. For example, two sessions

from User-7 was confused as User-5 and one session from

User-5 was confused as User-7. This can indicate some

similarity in the data for these two users.

Table 3. Confusion matrix for Experiment-1

Each row corresponds to a user. All the sessions from a user

indicated by the row label is placed on that row based on how

the session was classified with the column label indicating the

classification.

The classifier was executed multiple times to reduce the

feature set to the lowest number of attributes that produce the

best results. Eliminating Session-Id and subsequently the Ear-

Used attributes, improved the system performance. Removing

any other attributes deteriorated the performance. The

remaining attributes constitute the feature set for this

Experiment. Following are the attributes that are identified as

part of the feature set for this Experiment.

Table 4. Feature-set for Experiment-1 with the chi-squared ranks

2) EXPERIMENT-2: PHONE ON-EAR (VARIANCES

ADDED)

This experiment is very similar to Experiment-1. Addition of

variances into the list of attributes is the only change from

Experiment-1.

Similar to the earlier experiment, attributes are evaluated

using Chi-squared Attribute Evaluator. Table-5 shows the

attributes with chi-squared ranks. From the rankings, it appears

that the variances are not as significant as the averages in

identifying the user. The Z and X averages are still the most

significant attributes.

Table 5. Attribute Ranking

The experiment is performed multiple times by eliminating

lowest significant attribute each time. It was observed that the

following set of attributes provided the best performance.

PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR

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Table 6. Feature-set for Experiment-2 with the chi-squared ranks

In terms of number of instances classified correctly, both

Experiment-1 and Experiment-2 performed equally, by

correctly identifying 28 of the 35 instances. However,

Experiment-2 had lower error values compared to Experiment-

1 signifying an improvement in the model performance. With

the 35-fold cross-validation, the model produced 80% success

rate. Table-6 shows the result summary from Weka.

Table 7. Classifier Results Summary for Experiment-2

Table 8. Confusion matrix for Experiment-2

Confusion matrix has a slight difference from Experiment-1.

An instance belonging to User-5 was earlier classified as User-

1; now it is classified as User-7. Either way, both models got

that particular instance wrongly classified.

3) EXPERIMENT 3: PHONE PICKING-UP

Phone picking-up phase is relatively more dynamic. There

are high variances in accelerometer readings during this phase.

There are two components that contribute to the acceleration –

gravity and the user movement.

The first task in this experiment is to separate the two

components. Even though it is impossible to completely

separate the two components, a reasonable approximation can

be made using a Low-pass Filter [17].

Following formula is used for finding the gravity component

with the low-pass filter [1].

Where

- gravity on X, Y or Z-axis for ith measurement.

- threshold which can be found by the following

formula. For this experiment a constant threshold of 0.8 is

assumed.

is the time-constant.

is the sampling rate.

- ith reading from the accelerometer.

As done earlier, the attributes are initially evaluated using

chi-squared attribute evaluator resulting in following rankings.

Table 9. Attribute Ranking

As observed on Table-9, the gravity acceleration on Z-axis

and session id have the lowest ranks in identifying the user

instance.

As discussed earlier, the feature set need to be reduced by

eliminating low ranking attributes with out reducing the system

performance. By following this reduction, following attributes

are determined as the feature set for this Experiment.

Table 10. Feature-set for Experiment-2 with the chi-squared ranks

Only "Session Id” was removed for this experiment. All other

attributes including Z_gvty contribute to the system

performance.

Table-10 shows the summary of results for experiment-3.

PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR

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Table 11. Classifier Results Summary

The experiment identified 29 of the 35 users correctly, with a

success rate of 82.86%. It can be deduced that phone picking-

up method is slightly better in identifying the user compared to

the phone holding position.

Table-12 shows the details of how each session was

classified.

Table 12. Confusion matrix for Experiment-3

It can be noted from the confusion matrix that this experiment

identified some users (for example, User-7) more reliably

compared to the previous experiments and vice versa.

4) EXPERIMENT 4: COMBINATION OF PHONE ON-EAR

AND PICKING-UP PHASES

In this experiment, the feature sets extracted from

experiment-2 and experiment-3 are combined. Following

shows a quick visualization of all the available attributes. Each

color indicates a distinct user. For example, the Ear-Used

attribute is same for all users except for User-3 and for a portion

of samples for User-4.

Fig. 3. Data visualization of how individual attributes stack-up for each

user represented by a distinct color.

The combined feature set is evaluated using the chi-squared

attribute evaluator. The results are provided in Table-13

Table 13. Attribute Ranking

Attribute reduction logic is performed again and found that

the following subset provided the best performance.

Table 14. Feature-set for Experiment-4 with the chi-squared ranks

Table-14 shows a summary of results from the classifier.

Table 15. Classifier Results Summary

The combined experiment has the highest performance

compared all previous experiments. This experiment identified

32 of 35 samples correctly with a success rate of 91.43%.

Table-16 is shows the classification. The darker diagonal

indicates the improvement in performance. 4 of the 7 users are

PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR

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identified with 100% accuracy. The rest of the users were

identified with 80% accuracy.

Table 16. Confusion matrix for Experiment-4

IV. CONCLUSIONS AND FUTURE WORK

The result of this study is promising. The final model built

by combining feature sets has a high performance. It shows

91.4% success rate in correctly predicting the user. It is exciting

to see that this soft biometric analysis can be used in identifying

a user with such accuracy.

This experiment could be improved in a number of ways. The

sample size selected is relatively small. The experiment could

be repeated with a larger sample size to confirm the findings.

The data collection method can be improved. Instead of

conducting all 5 sessions in a single stretch, the data collection

could be done in phases with different sessions from each user

collected on different days. The direction were the user was

facing was neither random nor supervised. Hence some of the

users were choosing a random direction and recording all

samples facing that same direction. There were four users who

turned towards approximately the same direction. Also, the

participants were standing still during the phone call. It would

be more realistic to have the users walk or make other

movements while making the call. Making such changes can

potentially bring down the performance of the model; but such

changes will result in a robust model that can be used in real-

world applications.

This study focused purely on Accelerometer sensor.

Combining this sensor data with other available sensors like

Gyroscope, Magnetometer, etc. can improve the performance

of the model further. This will be another area to explore in the

future.

This model can be used in combination with other biometric

systems like voice recognition, face recognitions, etc. Such a

system that combines multiple biometric techniques for

continuous authentication could become a foundation for high

security systems in future.

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