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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