biometric system design for handheld devices

Biometric System Design for Handheld Devices

Team 4 Naif Alotaibi, Rich Barilla, Francisco

Betances, Aditya Chohan, Alexandra Garcia, Alexander Gazarov,

Mantie Reid, and Vinnie Monaco

Outline

• Introduction• Related Work• Background• Methodology• Data Collection

Introduction

Mobile devices become

pervasive

Security issues become more

important

Simple pin or pattern is the only defense

There is a need for constant

authentication

Related Work

• Researchers at University of Hong Kong– Single-Touch, Multi-Touch, Drag– Neural network, error rate of 7.8%

• 2012 ACM SIGCHI Conference on Human Factors in Computing Systems– 90% accuracy (single gestures)– Improvement using multiple gestures

(sequence)

• Researchers at the University of Houston– Graphic Touch Gesture Feature (GTGF)– Flick up/down, flick right/left, zoom in/out – Converting touch traces to images– Low Equal Error Rate of 2.62%

• Mobile device picking up motion– Trajectory and angle– Error rate of 6.13%– Accuracy declines with user movement

Background

• Touchscreen– X and Y coordinates of the touch (position) – Pressure of the touch– Size of the contact area– State change (Ex. Up, Down, etc) – Multi-touch screens report multiple movement

traces at the same time using pointers.

– Motion sensors: measure acceleration and rotational forces.

– Position sensors: used for capturing data about the physical position of the device

– Useful sensors for developing our system: • Accelerometer: measure the acceleration applied to the device. • Gyroscope: measures the rotation around the device's axis. • Orientation: measure the position relative to the earth's frame of

reference

Sensors

Position Motion Environment

Raw data capture

Devi

ce c

onfig

urati

on

Sens

or-b

ased

dat

a

Touc

h sc

reen

dat

aPointer coordinatesTouch action (down, move, or up)Finger pressure on the screenSize and shape of the touched area

AccelerometerGravityRotation

System versionDevice orientationScreen sizeScreen density

System architecture

Main activity (with

WebView)

Biometric event

Event buffer

SQLite database

Data file

Network

Settings activity

Data Collection

Data Collection

Results

• 98% accuracy within single session• 25% with one session as test and the

other as training data

Thank You!