Yang J., Poh N. (ed.) Recent Application in Biometrics
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Real-Time Stress Detection by means of Physiological Signals 17
Evidently, the longer t
T
and t
acq
, the more accurate the system is. However, in real
applications, time is the most valued asset, and therefore, a balance among t
T
, t
acq
,TSDand
TNSD must be achieved.
These temporal parameters are fixed during the validation step, and remain constant during
the testing stage.
Figure 4 provides information about how TSD varies in relation to different values of t
T
and
t
acq
.
5
10 15
20
0.94
0.95
0.96
0.97
0.98
t
acq
(s)
TESD Performance
t
T
= 5s
t
T
= 10s
t
T
= 17s
Fig. 4. Relation between TESD Performance and time to obtain template
(t
T
) and acquisition
time
(t
acq
). These values were obtained with ρ
th
= 0.27.
Reader can notice how the performance in detecting stress (TSD) increases as t
T
and t
acq
do
so. In fact, a TESD
= 99.16% (Figure 4, ) is obtained with t
T
= 17s and t
acq
= 17s,
which means that physiological signals GSR and HR from an individual are measured during
t
T
= 17s, and furthermore, that in subsequent accesses, such an individual must present their
physiological signals during t
acq
= 17s, so the system can decide to what extent is under
stress.
As before, the results obtained in this section are achieved for a given scheme, concretely
Fuzzy Logic. In order to obtain the best combination of parameters, this procedure must be
repeated for each scheme and implementation.
7.4 Evaluation performance
In order to evaluate the performance of the proposed approaches, the procedure presented
previously was carried out for every system. A detailed description of these results is far
beyond the scope of this section and therefore, Table 6 is presented to compared former
methods when detecting stress. Reader may notice that the performance of the proposed
methods depends on the temporal parameters t
T
and t
acq
, all of them measured in seconds.
The best result in every scheme is achieved with scheme BL1+HV which means that for an
accurate stress detection, only two tasks are required: a relaxing situation and a stressing
39
Real-Time Stress Detection by Means of Physiological Signals
18 Will-be-set-by-IN-TECH
GMM k-NN Disc. Anal. SVM Fuzzy Logic
(t
T
= 5, (t
T
= 5, (t
T
= 7, (t
T
= 5, (t
T
= 7,
t
acq
= 10) t
acq
= 5) t
acq
= 10) t
acq
= 10) t
acq
= 10)
TSD 95.1 ± 0.2 92.8 ± 0.4 95.6 ± 0.3 95.6 ± 0.4 99.5 ± 0.3
TNSD
86.3 ± 0.4 97.3 ± 1.3 96.7 ± 0.4 96.7 ± 0.3 97.4 ± 0.2
Table 6. Comparative stress detection performances. Best result is achieved with fuzzy logic,
although the rest of the results are competitive when compared to those obtained within
literature. Temporal parameters are provided in seconds and rates in percentage (%).
Reference Stress Detection Physiological Population
Rate
(%) Signals
Healey & Picard (2005) 97.4% ECG, EMG, RR, GSR Not provided
Wagner et al. (2005) 79.5-96.6% ECG, EMG, RR, GSR 1 subject
Cai & Lin (2007) 85-96% BVP, ST, RR, GSR Not provided
Guang-yuan & Min (2009) 75-85% ECG, EMG, RR, GSR 1 subject
Kulic & Croft (2005) 76% ECG, EMG, GSR 8 subjects
Sharawi et al. (2008) 60-78% ST, GSR 35 subjects
Best of our proposed methods 99.5% HR, GSR 80 subjects
Table 7. A comparison between approaches comparing stress detection rates, physiological
signals and population involved. The initials ST stand for Skin Temperature.
situation. An outstanding result, since it allows to decrease (in terms of time) the template
extraction step, among other aspects discussed in posterior Section 8.
The conclusion is that stress can be detected by means of fuzzy logic with an accuracy of
99.5% recording the signal of the user during 10 seconds to create the template and 7 seconds
for stress detection. These results highlight the improvement achieved in comparison to other
approaches, showed in Table 7, providing the following parameters to be compared: Stress
Detection rate (TSD), the physiological signals involved and the population used to evaluate
the proposed approach. This improvement is achieved not only in terms of accuracy in stress
detection, but also in relation to the number of physiological signals (only HR and GSR) and
the population.
8. Conclusions and future work
The proposed stress detection systems are able to detect stress by using only two physiological
signals (HR and GSR) providing a precise output indicating to what extent a user is under a
stressing stimulus.
In addition, HR and GSR allows a plausible future integration of former proposed systems on
current biometric systems, achieving and increase in the overall security.
Main characteristics of the proposed systems regard non-invasiveness, fast-oriented
implementation and an outstanding accuracy in detecting stress when compared to previous
approaches in literature.
In other words, the system can detect stress almost instantly, allowing a possible integration
in real-time systems. Notice that only two physiological signals are involved in contrast
to the amount of features required to elucidate on the stress degree provided by previous
approaches.
40
Recent Application in Biometrics
Real-Time Stress Detection by means of Physiological Signals 19
An individualization of not only the template T ,butalsoρ
th
, t
T
and t
acq
must be adapted for
each individual, so that the overall performance can be increased.
These parameters (ρ
th
, t
T
and t
acq
) have been fixed for the whole database within this work,
and therefore, if a different version of these parameters is considered for each individual, then
the accuracy of the system could be increased. This implementation remains as future work.
The database acquisition was based on psychological experiments carried out by expert
psychologists. These experiments ensure that stressing situations are provoked on an
individual, validating posterior HR and GSR acquisitions.
This paper provides a decision system able to detect stress with an accuracy of 99.5% using
fuzzy logic and 10 seconds to extract the stress template and 7 seconds to detect stress on an
individual using two physiological signals HR and GSR measured only during two tasks: a
stressing task and a relaxing stage.
The rest of the approaches are also competitives in terms of computational cost and
performance. This results are achieved due to the fact that the stress template provides precise
information on the state of mind of individuals, coming up with an innovative concept in
stress detection. A combination of approaches is regarded as future work.
Finally, these systems may be applicable in scenarios related to aliveness detection (e.g.,
detecting if an individual is accessing a biometric system with an amputated finger), civil
applications (e.g., driver control), withdrawing money from a cash dispenser, electronic voting
(e.g., someone is forced to emit a certain vote) and so forth. Moreover, future research entails
an integration in mobile devices.
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44
Recent Application in Biometrics
Stefano Tennina
1
, Luigi Pomante
2
, Francesco Tarquini
2
, Roberto Alesii
2
,
Fabio Graziosi
2
, Fortunato Santucci
2
and Marco Di Renzo
3
1
CISTER Research Unit, ISEP/IPP Rua Dr. António Bernardino de Almeida, Porto
2
Dept. of Electrical and Information Engineering and Center of Excellence in Research
DEWS, University of L’Aquila, Poggio di Roio, L’Aquila (AQ)
3
Laboratory of Signals and Systems (L2S), CNRS - SUPELEC - Univ Paris-Sud 3 rue
Joliot-Curie, Gif-sur-Yvette (Paris)
1
Portugal
2
Italy
3
France
1. Introduction
Due to the relevant innovations in the ICT domain, today, a lot of services is being provided
with a self–service approach to an even more great number of people simplifying several
tasks of everyday life (e.g., cash retrieval by ATM, remote-banking, etc.). The key aspect
to fully enable such services and make them wide accepted by peopole is the possibility
to reliably count on biometric identification mechanisms (Adeoye, 2010; BTAM, 2010; Elliott
et al., 2007; Li & Zhang, 2010; Sonkamble et al., 2010). Some of them are already exploited in
several real–life scenarios, like the Access Control–Border Management in Hong Kong or the
Access Control–Restricted Area Access by Canadian Air Transport Authority (BTAM, 2010).
However, some of such services could be very critical and so, their provisioning, should be
managed very carefully in order to avoid the possibility of malicious operations. So, the best
way to support the evolution of automatic services providing is to develop a system, also
automatic, which is able to trust in a secure and flexible way the identity of people that need
to access such services. Such a system should be of easy integration in several scenarios,
especially with respect to existing infrastructures, and should be designed to respect all the
relevant privacy issues while providing to the users all the feelings (about reliability, safety
and usability) needed to make the system acceptable.
In such a context, this book chapter aims at presenting an automatic personal identification
system developed by WEST Aquila (WESTAquila, 2010). The system, described in detail later,
exploits the recent advances in the biometric and heterogeneous wireless networks fields to
provide a true authentication platform supporting several services encompassing physical
access (e.g., to restricted areas or vehicles) as well as logical access (e.g., to personal services
like e-banking) management. This is realized by maintaining a full control over critical data
(biometric) that are used for the authentication. In fact, the main component of the system is
Automatic Personal Identification System for
Security in Critical Services: Two Case Studies
Based on a Wireless Biometric Badge
3
2 Will-be-set-by-IN-TECH
a novel biometric badge, i.e., a smartcard equipped with a biometric reader (i.e., a fingerprint
reader) and a short–medium range wireless transceiver which allow the identification of both
the card and the card owner. In other words, it constitutes a system–on–badge: when required,
the card owner is identified through an on–system biometric matching and only the result of
such a matching is sent (appropriately ciphered) through the wireless interface towards the
rest of the system. Therefore, personal biometric data is always under the full control of its
owner, leading to high levels of security and privacy protection.
The intended content of this chapter will be to illustrate the badge as a biometric system and its
usage in two case studies: (i) physical access of authorized people in a restricted area, which
involves also physical positioning of the badge owner and (ii) logical access of authorized
people in an e-banking–like scenario.
2. System architecture description
The proposed system is composed by a set of elements (Fig. 1) enabling high level of flexibility
and reliability, needed to make this proposal as a reference in the field of personal automatic
identification systems, where particular emphasis is on aspects like robustness, ease–of–use
and privacy.
Fig. 1. Logical System Architecture
2.1 Biometric Badge (BB)
The key point of the proposed system is our embedded biometric badge (Fig. 2), which is a
“system–on–badge” performing four main tasks: (i) enable the localization of its owner using
distributed positioning techniques, (ii) scan and verify fingerprints of people, (iii) check if
an user is the badge’s owner based on fingerprint matching, and (iv) send related outcomes
46
Recent Application in Biometrics
Automatic Personal Identification System for Security in Critical Services: Two Case Studies based on a Wireless Biometric Badge 3
wirelessly to the rest of the system (e.g., the DU which interconnects the badge to the rest of
the infrastructure), without the need to transmit the owners’ biometric data over the wireless
medium (so, in a secure way from the point of view of transmitting critical data of the users).
Fig. 2. WEST Aquila’s Biometric Badge – components
The badge is equipped with:
• the Texas Instruments’ SoC CC2430 (TI, 2009), which embeds a 8051 micro controller and
a CC2420 radio transceiver, compliant with the IEEE 802.15.4 (IEEE, 2006) standard. It is
used for wireless medium-range communications, as well as localization operations;
• a fingerprint sensor reader with its embedded “companion chip” provided by
UPEK (UPEK, 2009). This chip is the key element for handling biometric data: it allows
to authenticate people based on fingerprint information, as well as store data in a memory
protected even from physical external attacks. Moreover, only this chip and the gateway
are aware on how to decode the messages they send to each other;
• a RFID tag based on the ISO15693 standard and its companion chip provided by
Montalbano Technology (Montalbano, 2009), which allow the microcontroller to get access
to data stored in the tag;
• a rechargeable battery, its driver to monitor the charge status, and a user interface with 8
leds and a push-button.
Such features allow the badge to support a full range of applications (Fig. 3), mainly due to
the embedded fingerprint reader enabling both civil and military use of such a technology in
a secure and safe way.
2.2 Distributed Unit (DU)
Every DU is logically constituted by a “Gateway” (GW), one or more “Readers” (RDs) and one
or more “Actuators” (ATs) communicating one another using wireless or wired technologies.
The whole system can rely on several DUs, networked through secure communications with
a Central Processing Station (CPS) and related controlled systems (Fig. 1). Each DU maintains
synchronization of data with the central system and allows secure communications among
the system components.
47
Automatic Personal Identification System for
Security in Critical Services: Two Case Studies Based on a Wireless Biometric Badge
4 Will-be-set-by-IN-TECH
Fig. 3. WEST Aquila’s Biometric Badge – features
2.3 Gateway (GW)
The Gateway is the central element of each DU. It communicates in a secure way with the
RDs, with the ATs and back with the Central Processing Station (CPS). Its main function is
to provide an interface between the BBs and the CPS, both upwards and downwards. In
the upwards flow, the GW collects data from BBs through its associated RDs, interprets the
information contained within each packet and sends it to the CPS. In the downwards flow, the
GW receives instructions from the CPS and controls accordingly the ATs to grant or deny the
access to the BB.
2.4 Reader (RD)
The Reader is the element for interfacing the DU with the BBs. Its role is to communicate
wirelessly with the BBs, and it uses two mechanisms: the IEEE 802.15.4 communication
standard and a proximity–based RFID technology. As a logical component of the system,
it is not allowed to locally decode the data, but it simply forwards it towards the GW over
a secure communication channel. The communication between the RD and the GW can be
either wired or wireless. In the latter case, it can be direct, when they are in the communication
range of each other, or indirect, i.e., multi-hop through intermediate RDs. When it is wireless
and indirect, then the RDs constitutes a network of readers (NRD), organized into a ZigBee
Cluster Tree (ZigBee, 2008), (Koubâa et. al, 2008).
2.5 Actuator (AT)
The Actuator is a device in direct contact with the Controlled System (Fig. 1) and it constitutes
the interface with the GW so that the operations needed to provide the requested services to
the BB owner are executed, when he/she has passed the authentication process, or the safety
procedures when the authentication fails are applied. Similarly to the NRD, multiple ATs
might form a multi–hop wireless network (NAT) to reach the GW.
48
Recent Application in Biometrics