Analysis of Ensemble Systems for Touchscreen Biometrics Using Cancellable Transformations

An Empirical Analysis of Ensemble Systems in
Cancellable Behavioural Biometrics: a Touch Screen
Dataset
Marcelo Damasceno1;2 A.M.P. Canuto2
1Federal Institute of Education, Science and Technology of Rio Grande do Norte - São Gonçalo do
Amarante
2Federal University of Rio Grande do Norte
12/05/2013
Marcelo Damasceno (IFRN) An Empirical Analysis of Ensemble Systems in Cancellable Behavioural1B2i/o0m5/e2t0r1ic3s: a T1ou/ c3h6 Screen

About
This paper analyzes the performance of ensemble systems in the context
of cancellable behavioural biometrics, more specifically a touch-screen
dataset.
The main aim of this work is to analyse the gain that the use of ensemble
systems in cancellable data can bring with respect to the behavioural
biometric context.

Outline
1 Introduction
2 Cancellable Transformations
3 Ensemble Systems
4 TouchAnalytics
5 Methodology
6 Results
7 Conclusion and Further Work
8 References

Introduction
Outline
1 Introduction
3 Ensemble Systems
4 TouchAnalytics
5 Methodology
6 Results
8 References

Introduction
User Verification
Currently most computer systems use individual username and password
to authenticate their users [1];
Username-password method brings some problems as the use of same
username and password for different services on the Internet and the
stress to remember secure, long and complex passwords;

Introduction
Biometrics
Biometrics can be considered as the science of establishing the identity
of a person using his/her anatomical and/or behavioural traits.
Biometric traits have a number of desirable properties, such as reliability,
convenience, universality, and so forth.
Because of these characteristics, biometrics has been increasingly
developed over the last years.

Introduction
Behavioural Biometrics
Unlike physical biometrics, behavioural biometrics are related to user be-haviour/
actions [3].
These biometrics use behavioural patterns, such as gait, typing or the
way in which a user uses a computer system.
The behavioural biometrics is non-intrusive, i.e, often information
collection is not perceived by users.

Introduction
Biometrics Problems
The biometric is permanently associated with a user and cannot be
revoked or cancelled if compromised [4].
If a biometric identifier is compromised, it is lost forever and possibly the
same happens for every application where the biometric is used.
The use of cancellable biometrics is being increasingly adopted to
address such security issues.

Introduction
Cancellable Biometrics
This approach uses transformed or intentionally-distorted biometric data
instead of original biometric data for authentication [5, 6].
There is a risk that using such transformed data will decrease the
performance of the biometric-based system.

Cancellable Transformations
Outline
1 Introduction
3 Ensemble Systems
4 TouchAnalytics
5 Methodology
6 Results
8 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Ensemble Systems in Cancellable Behavioura1l2B/0io5m/2e0t1r3ics: a1T0ou/ c3h6 Screen

Cancellable Transformation
The non-invertible transformation functions can transform the biometric
data in a way that it is computationally impossible to get the original form;
The distorted data brings some undesired consequences as high
variance, what makes more difficult the users identification;

Transformation Functions
1 Interpolation: Based on polynomial interpolations;
2 BioHashing: Characterized by transforming the original biometric into a
non-invertible binary sequence;
3 BioConvolving: The transformed functions are created through linear
combinations of sub-parts of the original biometric template;
4 DoubleSum: Consists of summing the attributes of the original biometric
model with two other attributes of the same sample;

Ensemble Systems
Outline
1 Introduction
3 Ensemble Systems
4 TouchAnalytics
5 Methodology
6 Results
8 References

Ensemble Systems
Ensemble Systems
These systems exploit the idea that different classifiers can offer
complementary information about patterns [7].
Figure 1 presents a general structure of an ensemble system, which is
composed of a set of N individual classifiers (ICn), organized in a parallel
way.

Ensemble Systems
Ensemble Systems
Figure : An illustration of the general framework of an ensemble system

TouchAnalytics
Outline
1 Introduction
3 Ensemble Systems
4 TouchAnalytics
5 Methodology
6 Results
8 References

TouchAnalytics
TouchAnalytics
The behavioural biometric modality used in this work is a touch screen
data, which represents a combination of strokes collected from
smartphones.
TouchAnalytics, was collected by Frank et al. [2]. They inform how the
data was collected, processed and some initial results

TouchAnalytics
TouchAnalytics
This dataset is composed of 30 attributes and all the attributes are
derived from the strokes obtained from 41 users.
Strokes are composed of horizontal and scrolling (vertical) movements.
The dataset was binarized because we use a verification process. It was
created a different dataset for each user.

TouchAnalytics
TouchAnalytics Scenarios
1 Inter Session: The goal is to authenticate users across multiple sessions
performed in the same day.
2 Inter Week: The goal is to authenticate users after in two different weeks
(the period of time between these two sessions is one week).
3 Intra Session: All the user data was used in the process, time
independently. In this scenario, we used a 10 fold cross-validation
process.

TouchAnalytics
TouchAnalytics Results
According to [8], the mean EER:
Intra Session are 0%: Within one session, most users do not
considerably change their touch behaviour;
Inter Session: 2% to 3%
Inter Week: 0% to 4%
This result indicates the behavioural biometrics (touch data) has good
perspectives in practical use.

Methodology
Outline
1 Introduction
3 Ensemble Systems
4 TouchAnalytics
5 Methodology
6 Results
8 References

Methodology
Methodology I I
This investigation will use only the Intra Session experiment.
The use of three ensemble structures is analysed:Bagging, Stacking and
Majority Voting
We applied two different ensemble sizes in Bagging: six and twelve
individual classifiers.
Stacking and Voting use 6 individual classifiers in their structure.

Methodology
Methodology II
k-NN and Logistic Regression was selected as combination methods for
ensembles generated by Stacking.
We use SVM and k-NN as individual classifiers in half-by-half proportion
in heterogeneous structures (Bagging and Voting).
The 10-fold cross-validation methodology was used in empirical analysis.
The Mann-Whitney statistical test with the confidence level is 95%
(a = 0:05) is applied to compare the s EER of ensemble systems applied
in cancellable data versions with the EER achieved in original data.

Results
Outline
1 Introduction
3 Ensemble Systems
4 TouchAnalytics
5 Methodology
6 Results
8 References

Results
Results and Discussion - Scrooling
Table : Median Results using Scrooling Traits
Method Original Interpolation BioHashing BioConvol. Double Sum
Bag_6_k 7:64:8 8:95:4 32:312:6 3:310:7 8:75:5
Bag_12_k 7:44:9 8:65:1 32:412:4 3:210:8 8:65:4
Bag_6_S 9:26:4 12:48:2 32:419 2:37:8 11:98:1
Bag_12_S 9:26:4 12:38 31:215:5 2:17:8 11:78:3
Stack_kSk 7:85:1 106:3 32:313:1 3:410:6 106:5
Stack_kSL 7:24:7 95:5 32:712:7 3:410:9 9:15:8
Voting 8:96:4 10:96:7 32:612:6 3:611 11:47:5
Shaded cells are statistically similar.
Bold values mean that the cancellable result was statistically better.

Results
Results and Discussion - Horizontal Strokes
Table : Median Results using Horizontal Traits
Method Original Interpolation BioHashing BioConvol. Double Sum
Bag_6_k 84:5 10:66:3 32:89:8 0:10:3 8:74:8
Bag_12_k 7:74:3 10:36:2 32:810:2 0:10:3 8:64:6
Bag_6_S 11:17:3 16:19 34:417:7 0:40:5 13:38:4
Bag_12_S 11:77:8 16:19:2 33:526 0:30:4 13:18:5
Stack_kSk 8:54:9 12:17:3 349:5 0:20:4 10:76:3
Stack_kSL 7:74:5 10:86:7 33:110 0:20:4 9:65:4
Voting 9:75:8 13:77:1 33:59:7 0:20:4 11:96:9

Results
Discussion I
Ensembles used in Interpolation and Double Sum data have similar
statistical results when compared with results achieved by the Original
datasets (Tables 1 and 2).
EER values in BioConvolving are statistical better than EER from Original
data, for both strokes directions and for all ensemble structures (Tables 1
and 2).
The use of ensemble systems in behavioural cancellable biometrics do
not deteriorate the EER results, comparing with the EER achieved by
Original data.
Our only exception was BioHashing transformation that achieves the
worst EER values, compared with all ensemble structures.
The use of ensemble structures improves the results using Interpolation,
BioConvolving and Double Sum functions in scrolling strokes compared
with results achieved in our previous work [2].

Results
Discussion II
These results show that we can use ensemble systems and cancellable
transformation in behavioural biometrics instead of the original data,
without deteriorating the performance of the biometric-based
authentication systems.

Conclusion and Further Work
Outline
1 Introduction
3 Ensemble Systems
4 TouchAnalytics
5 Methodology
6 Results
8 References

Conclusion I
In this paper, we performed a comparative analysis of well-known
ensemble structures applied to cancellable behavioural biometrics.
Four cancellable functions (Interpo- lation, BioHashing, BioConvolving
and Double Sum) were applied in this dataset to demonstrate the
importance and perspectives of cancellable behavioural biometrics.
The Interpolation and Double Sum results were statistical similar to
Original results. The mean EER of Original dataset varies from 7.4% to
11.7%, while in Interpolation dataset, the EER varies between 8.6% and
16.1%.
Double Sum dataset, EER varies from 8.6% to 13.3%.
The mean ERR of BioConvolving dataset varies from 0.1% and 3.60%,
and it was statistically superior than all other datasets, for all ensembles
structures.

Conclusion II
The results obtained by BioConvolving are promising and indicate that the
use of cancellable behavioural biometrics can have a positive effect in
biometric-based authentication systems.
The results achieved in this paper are better than in our previous [2]. This
shows that the use of ensembles methods are better than using single
classifiers.
We have demonstrated that the use of a transformation function usually
provides similar or better performance than the original biometric data
As a future work, in order to improve the results we will use more
classifiers as MultiLayer Perceptrons, optimize cancelable function
parameters and use of multimodal biometrics.

References
Outline
1 Introduction
3 Ensemble Systems
4 TouchAnalytics
5 Methodology
6 Results
8 References

References
References I
W. Jackson, “Antisec hackers claim theft of military e-mails from booz
allen,” Internet, Julho 2011, acessado em Novembro de 2011. [Online].
Available: http://gcn.com/articles/2011/07/11/
antisec-booz-allen-hack-military-emails.aspx
M. Damasceno, A. M. P. Canuto, An Empirical Analysis of Cancellable
Transformations in a Behavioral Biometric Modality. In: IEEE. 13th
Conference on Hybrid Intelligent Systems. Tunis, Tunisia: IEEE, 2013. In
press.
K. Revett, Behavioral Biometrics: a Remote Access Approach. John
Wiley Sons, Ltd, 2008.
A. K. Jain, K. Nandakumar, and A. Nagar, “Biometric template security,” in
EURASIP Journal On Advances in Signal Processing, 2008.

References
References II
C. Lee and J. Kim, “Cancelable fingerprint templates using
minutiae-based bit-strings,” Journal of Network and Computer
Applications, vol. 33, no. 3, pp. 236 – 246, 2010.
A. Nagar, K. Nandakumar, and A. K. Jain, “A hybrid biometric
cryptosystem for securing fingerprint minutiae templates,” Pattern Recogn.
Lett., vol. 31, pp. 733–741, June 2010.
A. M. P. Canuto, M. Abreu, L. Oliveira, J. C. X. Jr., and A. Santos,
“Investigating the influence of the choice of the ensemble members in
accuracy and diversity of selection-based and fusion-based methods for
ensembles,” Patt Recogn Letters, vol. 28, no. 4, pp. 472–486, 2007.

References
References III
M. Frank, R. Biedert, E. Ma, I. Martinovic, and D. Song, “Touchalytics: On
the Applicability of Touchscreen Input as a Behavioral Biometric for
Continuous Authentication,” in IEEE Transactions on Information
Forensics and Security, vol. 8, no. 1, 2013, pp. 136–148. [Online].
Available: http://www.mariofrank.net/touchalytics/

References
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Analysis of Ensemble Systems for Touchscreen Biometrics Using Cancellable Transformations

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