08.06.2007 1 Iris Recognition Under Various Degradation Models Hans Christian Sagbakken.
-
Upload
johnny-sutor -
Category
Documents
-
view
215 -
download
0
Transcript of 08.06.2007 1 Iris Recognition Under Various Degradation Models Hans Christian Sagbakken.
08.06.2007 1
Iris Recognition Iris Recognition Under Various Under Various
Degradation ModelsDegradation Models
Hans Christian SagbakkenHans Christian Sagbakken
08.06.2007 2Hans Christian Sagbakken
OutlineOutline
IntroductionIntroduction Scope and research Scope and research
questionsquestions Experimental setupExperimental setup Results Results ConclusionsConclusions
08.06.2007 3Hans Christian Sagbakken
IntroductionIntroduction
08.06.2007 4Hans Christian Sagbakken
Biometrics technologyBiometrics technology
Biometrics refers to technologies that Biometrics refers to technologies that measure and analyze human physical measure and analyze human physical and behavioural characteristics and behavioural characteristics
Examples of characteristics include Examples of characteristics include fingerprints, eye retinas and irises, fingerprints, eye retinas and irises, facial patterns and handfacial patterns and hand measurementsmeasurements
Two main application:Two main application: Verification (mobile banking) Verification (mobile banking) Identification (security control)Identification (security control)
08.06.2007 5Hans Christian Sagbakken
Example of an iris Example of an iris patternpattern
08.06.2007 6Hans Christian Sagbakken
Iris recognition prosessIris recognition prosess1. Segmentation prosess
2. Normalisation prosess
3. Iris code generation
Comparison
4. Comparison/decision
08.06.2007 7Hans Christian Sagbakken
Scope and research Scope and research questionsquestions
08.06.2007 8Hans Christian Sagbakken
Research questionsResearch questions
1. Under which conditions is iris-1. Under which conditions is iris-based recognition feasible?based recognition feasible?
2. Which filter to perform under 2. Which filter to perform under certain degradation conditions?certain degradation conditions?
08.06.2007 9Hans Christian Sagbakken
Scope of the thesisScope of the thesis
The thesis was restricted to experiments The thesis was restricted to experiments in MATLAB onlyin MATLAB only
Adapt Libor Masek’s open source code Adapt Libor Masek’s open source code for the experiments (different filters, for the experiments (different filters, inter-class and intra-class comparisions)inter-class and intra-class comparisions)
The iris images degradations are The iris images degradations are simulated in MATLAB with different simulated in MATLAB with different parameters (to find the best filter under parameters (to find the best filter under different conditions)different conditions)
08.06.2007 10Hans Christian Sagbakken
Experimental setupExperimental setup
08.06.2007 11Hans Christian Sagbakken
ImplementationImplementation
Expanded Libor Masek’s open source Expanded Libor Masek’s open source code for iris recognition with four code for iris recognition with four filtersfilters
Log-Gabor filter (9600 bit, original filter)Log-Gabor filter (9600 bit, original filter) 702-bit Haar wavelet filter702-bit Haar wavelet filter 87-bit Haar wavelet filter87-bit Haar wavelet filter Log of Gaussian filter (9600 bit)Log of Gaussian filter (9600 bit)
Expanded the search function with Expanded the search function with inter-class and intra-class inter-class and intra-class comparisonscomparisons
08.06.2007 12Hans Christian Sagbakken
Iris databaseIris database The filters are tested on 500 images from the The filters are tested on 500 images from the
UBiris database. Five images per person for UBiris database. Five images per person for 100 persons.100 persons.
The images are simulated with different The images are simulated with different paramentersparamenters Add noise in the image database (Gaussian noise) Add noise in the image database (Gaussian noise) Add blur in the image databaseAdd blur in the image database Change the light intensity in the image databaseChange the light intensity in the image database Rotate the images in the databaseRotate the images in the database
08.06.2007 13Hans Christian Sagbakken
EvaluationEvaluation For each filter under different conditions, the For each filter under different conditions, the
False Acceptance Rate (FAR) and False False Acceptance Rate (FAR) and False Rejection Rate (FRR) are computedRejection Rate (FRR) are computed
Inter-class comparisons (to experiment with Inter-class comparisons (to experiment with FAR). For each test 123,750 comparisons are FAR). For each test 123,750 comparisons are donedone
Intra-class comparisons (to experiment with Intra-class comparisons (to experiment with FRR). For each test 1000 comparisons are FRR). For each test 1000 comparisons are donedone
Totally 6,930,000 inter-class and 56,000 intra-Totally 6,930,000 inter-class and 56,000 intra-class comparisons are performed.class comparisons are performed.
08.06.2007 14Hans Christian Sagbakken
Example of hamming Example of hamming distributiondistribution
Inter-class comparisons
Intra-class comparisons
08.06.2007 15Hans Christian Sagbakken
Example of FAR and FRRExample of FAR and FRR
Optimal threshold value = 0.32
08.06.2007 16Hans Christian Sagbakken
ResultsResults
08.06.2007 17Hans Christian Sagbakken
Results under noisy Results under noisy conditionsconditions
Støyvarianse 0.002 Støyvarianse 0.004 Støyvarianse 0.006
Terskel FRR FAR Terskel FRR FAR Terskel FRR FAR
702-bit Haar wavelet 0.33 0.265 0.189 0.35 0.262 0.249 0.36 0.315 0.302
87-bit Haar wavelet 0.42 0.304 0.298 0.42 0.397 0.291 0.43 0.392 0.344
Log-Gabor 0.42 0.280 0.196 0.43 0.412 0.277 0.44 0.528 0.295
Log of Gaussian 0.39 0.259 0.191 0.41 0.300 0.287 0.42 0.384 0.336
08.06.2007 18Hans Christian Sagbakken
Results under blur Results under blur conditionsconditions
Blur radius 2 Blur radius 4 Blur radius 6
Terskel FRR FAR Terskel FRR FAR Terskel FRR FAR
702-bit Haar wavelet 0.30 0.126 0.118 0.31 0.134 0.122 0.34 0.221 0.180
87-bit Haar wvelet 0.37 0.181 0.128 0.40 0.178 0.154 0.43 0.216 0.210
Log-Gabor 0.39 0.127 0.109 0.39 0.138 0.128 0.41 0.263 0.195
Log of Gaussian 0.30 0.137 0.135 0.27 0.155 0.148 0.25 0.251 0.223
08.06.2007 19Hans Christian Sagbakken
Results under light Results under light changeschanges
Lysintensitet -10% Lysintensitet -5% Lysintensitet +5% Lysintensitet +10%
Terskel FRR FAR Terskel FRR FAR Terskel FRR FAR Terskel FRR FAR
702-bit Haar wavelet 0.26 0.225 0.172 0.29 0.175 0.159 0.32 0.129 0.128 0.33 0.130 0.113
87-bit Haar wvelet 0.37 0.243 0.207 0.41 0.228 0.229 0.43 0.230 0.224 0.43 0.256 0.217
Log-Gabor 0.37 0.205 0.198 0.39 0.168 0.127 0.42 0.125 0.120 0.43 0.146 0.122
Log of Gaussian 0.31 0.212 0.176 0.31 0.198 0.156 0.26 0.251 0.210 0.33 0.153 0.126
08.06.2007 20Hans Christian Sagbakken
Results under rotationResults under rotation
2 grader 3 grader 4 grader
Terskel FRR FAR Terskel FRR FAR Terskel FRR FAR
702-bit Haar wavelet 0.35 0.230 0.209 0.32 0.179 0.151 0.35 0.229 0.213
87-bit Haar wavelet 0.40 0.195 0.230 0.40 0.269 0.187 0.42 0.281 0.259
Log-Gabor 0.40 0.148 0.139 0.40 0.160 0.135 0.42 0.206 0.174
Log of Gaussian 0.34 0.178 0.147 0.35 0.173 0.172 0.37 0.277 0.232
08.06.2007 21Hans Christian Sagbakken
ConclusionsConclusions Under noisy conditions the best results where Under noisy conditions the best results where
achieved with 702-bit Haar wavelet filterachieved with 702-bit Haar wavelet filter Under blur conditions the best results where Under blur conditions the best results where
achieved with 702-bit Haar wavelet filterachieved with 702-bit Haar wavelet filter Under light changes the best results where Under light changes the best results where
achieved with 702-bit Haar wavelet filter and achieved with 702-bit Haar wavelet filter and Log-Gabor filterLog-Gabor filter
Under rotation the best results where achieved Under rotation the best results where achieved with Log-Gabor filterwith Log-Gabor filter
Totally the best filter is 702-bit Haar wavelet Totally the best filter is 702-bit Haar wavelet filterfilter
08.06.2007 22Hans Christian Sagbakken
Questions???Questions???