ARCHITECTURE OF INFORMATION SYSTEM FOR

INTELLIGENT CASH MACHINE

Krystian Ignasiak, Marcin Morgoś

Institute of Radioelectronics, Information Technology & Electronics Department, Warsaw University of Technology

Nowowiejska 15/19, 00-665 Warsaw, Poland

Surachai Ongkittikul

I-Lab/Centre for Communication Systems Research (CCSR), University of Surrey

Guildford GU2 7XH, Surrey, UK

Keywords: Cash machine simulator, software architecture, Computer Vision Markup Language.

Abstract: The paper summarizes the idea of the intelligent cash machine simulator that can be a very flexible

environment to test new algorithms in image processing. The use cases for such a cash machine are

discussed. Some data workflow is presented, and finally, an architecture is proposed for integration of

different software modules without imposing constraints on software platforms and tools. The proposal is

based on XML as a common language to exchange data.

1 INTRODUCTION

Nowadays, all electronic equipment may become

intelligent if appropriate logic is implemented inside.

Cash machines are sensitive to many situations

occurring in front of them. This concerns safety of

cash transactions and the safety of the user of the

machine – a bank customer. A cash machine can

also be perceived as an element of a surveillance

system.

This paper presents some system concepts of a

software architecture, proposed for a cash machine

simulator that will become user-friendly, intelligent

and safe. The proposed cash machine simulator has

the same functions as an ordinary cash machine but

extends them with new features. The intelligence of

the simulator is enhanced and realized by:

• Introducing a voice communication channel;

the cash machine can be instructed by voice

commands, as well as being able to inform

the user about actions to take and the status of

the transaction via synthetic speech.

• Introducing a visual communication channel

from user to the machine; the user is

observed by a set of cameras; the software

keeps track of the user’s behavior, analyses

the behavior, interprets their gestures, and

takes proper decisions.

• Introducing elements of a surveillance system

into the machine; audiovisual sequences can

be recorded and stored in the database; the

AV material can be enriched by metadata

about anything unusual that happened during

ordinary cash machine transaction.

On the other hand such an automatic teller will

increase the safety of transactions and personal

safety of the customer. The cash machine uses

advanced customer authentication based not only on

standard PIN codes but also on face recognition and

verification, speech recognition and other signals

from different modalities available at the cash

machine (e.g. fingerprints). Unusual behavior by the

user or the occurrence of an unusual situation in

front of the cash machine can be detected and

security services can be informed about that. In such

a case the audiovisual (and multimodal) sequence is

stored in a database and it documents the marked

disturbance.

Imagine that you approach the cash machine and

it welcomes you, recognizes your gender, age,

maybe mood, and it modifies its behavior with

respect to these factors. The machine helps you to

carry out the basic operation—withdraw the cash

401

Ignasiak K., Morgo

s M. and Ongkittikul S. (2007).

ARCHITECTURE OF INFORMATION SYSTEM FOR INTELLIGENT CASH MACHINE.

In Proceedings of the Second International Conference on Signal Processing and Multimedia Applications, pages 391-395

DOI: 10.5220/0002139903910395

 SciTePress

from your bank account—faster and with greater

ease. For example, users can define simple gestures

for common tasks, such as withdrawing money. The

proposed system enables also the bank to offer new

services.

We want to start with a simulation of such an

intelligent cash machine, hoping it would be

implemented as a real device once its usefulness is

proved and tested. On the other hand our goal is to

create an environment to test new algorithms and

ideas in a manner that does not impose on use of

particular software platforms and software tools.

However we propose a particular solution to test the

assumptions. The simulator is a very flexible

experimental environment to test new algorithms

and ideas, prior to implementation in real not only in

cash machine environment.

We start from the discussion of the use cases for

the cash machine to set up the requirements of the

system, then we present the workflow of data and

tasks within it. Finally we propose the software

architecture to be used for the cash machine

simulator.

2 USE CASES FOR CASH

MACHINE SIMULATOR

The analysis of use cases (OMG, 2005) for cash

machine should start from the user’s perspective.

The main cash machine services offered to the user

are withdrawal of money from his/her bank account,

checking the account’s status and—for our

purposes—logging into the system to access

advanced functions (cf. Figure 1).

In fact we can identify modules of the system

preparing the use cases and set the system’s initial

requirements. The first module identified is the cash

machine itself. It is used by ordinary user (bank’s

customer) as well as it is used by bank’s service to

configure the cash machine, calibrate devices such

as cameras, microphones, fingerprint sensor etc.

(cf. Figure 2).

The most important module for the simulator is

the Conversational Agent module. Use cases for this

module are depicted in Figure 3. The Conversational

Agent represents the artificial intelligence of the

system. The agent is updated by the environment

analysis module and welcomes and invites

pedestrians to use the machine. The agent controls

the interaction between the user and the machine

following the sequence of appropriate actions

needed to service the user.

Authorization module

Cash machine

Check account balance

and status

Withdraw or deposit

cash

User

Verify face, voice,

fingerprints

Verify PIN / pasword

<<include>>

Figure 1: Use cases for the user.

Cash machine

Service

person

Browse security alerts

Check operational

status

Configure cash

machine

Calibrate devices

Figure 2: Use cases for the bank’s service.

The Conversational Agent controls the behavior

of the 3D avatar that represents the agent itself in the

process of user-machine interaction. It controls the

speech synthesis by sending proper signals to speech

synthesis module, as well as controlling the

visualization of the avatar by sending signals to

visualization module. This includes rendering the 3D

avatar and animating its face together with emotions

to be expressed on the avatar’s face.

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402

Speech synthesis module

Visualization module

Conversational Agent module

Environment analysis module

AV storage and retrival module

Conversational

Agent (AI)

Controll AV recording

and storage

Control functions

of cash machine

Send aletrs to bank

office / service

Control user - machine

interaction

Record AV material Hide additional data

<<include>>

Invite / welcome

pedestrians

Logout / discard users

- Control 3D avatar

- Control speech synthesis

- Control facial expression

<<include>>

Analyse environment

Analyse user's voice/

face / gestures / body

emotions

<<extend>>

Generate speech

Render / animate

3D avatar

Render / animate

3D face with facial

emotions

<<include>>

Figure 3: Use cases for Conversational Agent.

The environment analysis module keeps track of

user’s voice, user’s body and face emotions to

update the Conversational Agent and to identify an

unusual user’s behavior at the cash machine. Besides

recognizing unusual user behavior, this module is

responsible for detecting unusual behavior in front

of the cash machine at all. It can detect pedestrians

carrying dangerous objects etc. Once such an

unusual behavior is identified, the agent sends alerts

to bank or police station, also it can change the

status of the machine into so called anti-attack

mode.

The gesture analysis part of the environment

analysis module aims to provide a convenient

human-machine interface for users. This gesture

analysis function is responsible for extracting the

user’s hand gestures. Gestures can be used by the

user to access the cash machines services, and can

also be an indication of mood to the machine.

There is a very important module present in the

system from surveillance point of view: AV storage

and retrieval. This module is responsible for storing

the audiovisual material taken by cameras and

microphones at the cash machine. One of its

functions is additional data hiding in such a way that

the material can not be modified by intruders.

Metadata hidden in the audiovisual material allow

the bank to state the origin of it.

The use cases in Figure 3 do not cover actions

for close interaction with bank services, because it is

beyond the scope of this paper. For example,

monitoring the amount of cash in the machine or

monitoring the funds available to the user are

functions that are not considered in the simulator.

3 WORKFLOW WITHIN THE

SIMULATOR

Having defined the use cases for the cash machine

one can define tasks to be performed within the

system and a workflow of them within the system. It

is not an easy task, as we do not want to specify the

algorithms to be implemented and tested within the

system.

However, we want to use the simulator to test

selected algorithms in the image processing field

that cover our interests within the Visnet II Network

of Excellence (Visnet II, 2006, Sadka, Skarbek,

2004).

We can start from calibration of the devices

connected to the cash machine – cameras for

example. They need to be calibrated prior to use

while some of algorithms to be implemented require

a set of three cameras for example. We have to

ensure stable cameras positions and parameters so

that the task of cameras calibration can be performed

only once.

The other exemplary task that is led up

continuously is the observation and analysis of the

environment of the cash machine to detect potential

user approach. This could be performed by human

ARCHITECTURE OF INFORMATION SYSTEM FOR INTELLIGENT CASH MACHINE

403

face detection in the frames of visual sequences

captured by cameras built into the machine. This is

the task performed by the environment analysis

module and it can be illustrated as the sequence

UML diagram (cf. Figure 4).

Cameras

module

Environment

analysis module

Face detection

module

take the next

video frame

frame ready

detect human

face

human face

present

AUT

Conversational

agent module

welcome

potential user

Figure 4: Exemplary sequence diagram within the cash

machine simulator system, AUT stands for Algorithm

Under Test – the part of the system being tested in the

particular experiment.

It is obvious from the Figure 4 that there is a

skeleton of the system that is present in every

particular implementation and there are modules that

are included for testing purposes, i.e. AUT –

Algorithm Under Test. However the system has to

define a common language for exchange the data

flowing between the modules. The best choice is

some XML application. This XML application must

provide the modules with the ability to exchange the

audiovisual data and the ability to exchange control

data. Audiovisual data create massive streams while

control data are rather sparse.

4 THE ARCHITECTURE

The architecture constitutes some software modules,

that are the core of the system and modules that can

be plugged into it for testing purposes. The core

modules cover the following:

• Cash machine module,

• Authorization module,

• Conversational Agent module,

• Audiovisual storage and retrieval module,

• Environment analysis module,

• Speech synthesis module, and

• Visualization module.

The modules communicate with each other using

Computer Vision Markup Language application,

CVML (List, Fisher, 2004). The media data and the

control signals are both encapsulated in XML syntax

that is understood by every module. The AUT being

plugged into the system must recognize a set of

commands from this syntax to process media and to

produce its output that will be probably sent to

another module for further processing.

The modules themselves have internal structure

to allow finer processing and controlling the AUT

being plugged into the system. The communication

between elements of modules relies on the same idea

of sending AV data and control data encapsulated

into CVML application.

Such a general architecture does not impose any

particular software platform and any particular

software tool that has to be used. The only

requirement is that the used tools are to understand

XML encapsulating media data and control data.

However we would propose a model solution. It

includes Java as a platform for generating graphical

user interfaces. The SWT (Standard Widget Toolkit,

http://www.eclipse.org.swt/) would be used instead

of AWT and Swing. For rendering purposes we

would propose some OpenGL package for Java, for

example JME (Java Monkey Engine,

http://www.jmonkeyengine.com/) – one of the most

efficient implementation of OpenGL to Java

mapping, that supports many OpenGL extensions.

As the IDE for building software we would

propose Ecplise platform (http://www.eclipse.org). It

has many advantages over other IDEs available. For

instance, from collaborative point of view, again, it

does not impose use of any particular programming

language, however it is fine suited for Java as a

result of many anonymous Java programmers

contributions.

The Eclipse IDE defines Rich Client Platform

(RCP) that enables close integration of tools being

created within the IDE. For example the JME

OpenGL engine is prepared within RCP concept.

Massive computations needed by tested

algorithms can be processed in other programming

languages or external mathematical packages, for

instance Scilab (http://www.scilab.org/). The Java

Native Interface can be used for this purpose or any

other method offered by the mathematical package

itself.

5 CONCLUSIONS

We have discussed the idea of the intelligent cash

machine through its use cases, data flow within the

system and sketch of the architecture. We have

proposed a set of tools well tailored to the needs of

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people testing their algorithms and ideas in image

processing field in general.

ACKNOWLEDGEMENTS

The work presented was developed within

VISNET II, a European Network of Excellence

(http://www.visnet-noe.org), funded under the

European Commission IST FP6 programme.

REFERENCES

List, T., Fisher, R. B., 2004, CVML – An XML-based

Computer Vision Markup Language. In International

Conference on Pattern Recognition Proceedings, 1,

789-792.

OMG, 2005, Unified Modelling Language: Superstructure,

version 2.0.

Sadka, A. H., Skarbek, W., 2004, European Union

Network of Excellence on Networked Audiovisual

Media Technologies – Visnet. In

Visnet@KKRRiT2004, Special Session at National

Conference on Radiocommunications and

Broadcasting, Warsaw, Poland.

Visnet II, Network of Excellence, 2006,

http://www.visnet-noe.org/.

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