User Verification System by William Baker, Arthur Evans, Lisa Jordan, Saurabh Pethe Client Dr.Cha

User Verification System

by

William Baker, Arthur Evans, Lisa Jordan, Saurabh Pethe

Client

Dr.Cha

Aim:

To improve confidence level by hybridizing multiple biometrics such as Face, Finger print, Handwriting, Hand geometry, Iris and Voice.

Confidence Level: Percentage of correct answers, valid user accepted and invalid user rejected.

To reduce false positive and false negative errors :- valid user rejected - invalid user accepted

Types of Biometrics decided for this project experiment:

• Face

• Handwriting

• Voice

• Finger print

biomouse Fingerprint

scanner

DigitalCamera

LCD Pentablet Microphone

Multi-modality Biometric AuthenticationMulti-modality Biometric Authentication

Embeded & Hybrid User Verification

system

System that requires user verification

Hand Writing features:

• Width• Height• Drag count• Total stroke time • Total stroke distance• Stroke direction sequence string• Acceleration

Tools used:

LCD Pen Tablet for data collection

Java application for feature extraction

Each person writes differently.

Face Recognition:

• Photos collected have to be properly sized and also be gray scale.

• Eigen face technology is used to calculate the mean face/value

• Recognition is done using Nearest Neighbor method.

Tools Used:

• Digital Camera for data collection

• Mathworks’ Matlab for training and recognition

Each person has different faces.

?Query

Face DB

Face Recognition SystemFace Recognition System

width, length

User 1

User 2

User1 s1 = ( 12 , 16 )

User1 s2 = ( 11 , 20 )

User2 s1 = ( 9 , 8 )

User2 s2 = ( 10 , 7 )

Truth features

MeasurementsMeasurements

slant

width

user1

user2

= user1?

Nearest Neighbor ClassifierNearest Neighbor Classifier

too slow for users to wait for the output.

Data Acquisition

Feature Extraction

Training an ANN

Classification System

Handwriting Done Done - -

Face Done ** ** **

Voice Done - - -

Finger print - - - -

Modality

Steps

Project Status

** - Eigen face and nearest neighbor methods used.

Advantages:

• Higher accuracy of determining an individual

• Reliable by having multiple recognition techniques or biometrics

• Increased security in companies

• Reduced amount of time to identify a suspect or criminal for law enforcement

• Difficult to challenge the system by forging names and mimicking voices making it virtually impossible to pass as someone else

• Possible use in a court of law to prove criminal cases

• Low maintenance software

Future Plan:

Handwriting training and classification.

Voice feature extraction methods

Finger print data collection

Demonstration

Handwriting

Face Recognition

Sub-classing with Java1. Data Collection Module

VoiceCollector.c lass HandW ritingCollector.c lass FaceCollector.c lass

DataCollector.c lassABST RACT

2. Feature Exctration Module

VoiceExtractor.c lass HandW ritingExtractor.c lass FaceExtractor.c lass

FeatureExtractor.classABST RACT