User Verification System by William Baker, Arthur Evans, Lisa Jordan, Saurabh Pethe Client Dr.Cha
Dec 22, 2015
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