1. 08.06.2007 1 Iris Recognition Under Various Degradation Models Hans Christian Sagbakken

2. 08.06.2007 2Hans Christian Sagbakken Outline Introduction Introduction Scope and research questions Scope and research questions Experimental setup Experimental setup Results Results Conclusions Conclusions

3. 08.06.2007 3Hans Christian Sagbakken Introduction

4. 08.06.2007 4Hans Christian Sagbakken Biometrics technology Biometrics refers to technologies that measure and analyze human physical and behavioural characteristics Biometrics refers to technologies that measure and analyze human physical and behavioural characteristics Examples of characteristics include fingerprints, eye retinas and irises, facial patterns and hand measurements Examples of characteristics include fingerprints, eye retinas and irises, facial patterns and hand measurements Two main application: Two main application: Verification (mobile banking) Verification (mobile banking) Identification (security control) Identification (security control)

5. 08.06.2007 5Hans Christian Sagbakken Example of an iris pattern

6. 08.06.2007 6Hans Christian Sagbakken Iris recognition prosess 1. Segmentation prosess 2. Normalisation prosess 3. Iris code generation Comparison 4. Comparison/decision

7. 08.06.2007 7Hans Christian Sagbakken Scope and research questions

8. 08.06.2007 8Hans Christian Sagbakken Research questions 1. Under which conditions is iris-based recognition feasible? 1. Under which conditions is iris-based recognition feasible? 2. Which filter to perform under certain degradation conditions? 2. Which filter to perform under certain degradation conditions?

9. 08.06.2007 9Hans Christian Sagbakken Scope of the thesis The thesis was restricted to experiments in MATLAB only The thesis was restricted to experiments in MATLAB only Adapt Libor Masek’s open source code for the experiments (different filters, inter-class and intra-class comparisions) Adapt Libor Masek’s open source code for the experiments (different filters, inter-class and intra-class comparisions) The iris images degradations are simulated in MATLAB with different parameters (to find the best filter under different conditions) The iris images degradations are simulated in MATLAB with different parameters (to find the best filter under different conditions)

10. 08.06.2007 10Hans Christian Sagbakken Experimental setup

11. 08.06.2007 11Hans Christian Sagbakken Implementation Expanded Libor Masek’s open source code for iris recognition with four filters Expanded Libor Masek’s open source code for iris recognition with four filters Log-Gabor filter (9600 bit, original filter) Log-Gabor filter (9600 bit, original filter) 702-bit Haar wavelet filter 702-bit Haar wavelet filter 87-bit Haar wavelet filter 87-bit Haar wavelet filter Log of Gaussian filter (9600 bit) Log of Gaussian filter (9600 bit) Expanded the search function with inter-class and intra-class comparisons Expanded the search function with inter-class and intra-class comparisons

12. 08.06.2007 12Hans Christian Sagbakken Iris database The filters are tested on 500 images from the UBiris database. Five images per person for 100 persons. The filters are tested on 500 images from the UBiris database. Five images per person for 100 persons. The images are simulated with different paramenters The images are simulated with different paramenters Add noise in the image database (Gaussian noise) Add noise in the image database (Gaussian noise) Add blur in the image database Add blur in the image database Change the light intensity in the image database Change the light intensity in the image database Rotate the images in the database Rotate the images in the database

13. 08.06.2007 13Hans Christian Sagbakken Evaluation For each filter under different conditions, the False Acceptance Rate (FAR) and False Rejection Rate (FRR) are computed For each filter under different conditions, the False Acceptance Rate (FAR) and False Rejection Rate (FRR) are computed Inter-class comparisons (to experiment with FAR). For each test 123,750 comparisons are done Inter-class comparisons (to experiment with FAR). For each test 123,750 comparisons are done Intra-class comparisons (to experiment with FRR). For each test 1000 comparisons are done Intra-class comparisons (to experiment with FRR). For each test 1000 comparisons are done Totally 6,930,000 inter-class and 56,000 intra-class comparisons are performed. Totally 6,930,000 inter-class and 56,000 intra-class comparisons are performed.

14. 08.06.2007 14Hans Christian Sagbakken Example of hamming distribution Inter-class comparisonsIntra-class comparisons

15. 08.06.2007 15Hans Christian Sagbakken Example of FAR and FRR Optimal threshold value = 0.32

16. 08.06.2007 16Hans Christian Sagbakken Results

17. 08.06.2007 17Hans Christian Sagbakken Results under noisy conditions Støyvarianse 0.002Støyvarianse 0.004Støyvarianse 0.006 TerskelFRRFARTerskelFRRFARTerskelFRRFAR 702-bit Haar wavelet0.330.2650.1890.350.2620.2490.360.3150.302 87-bit Haar wavelet0.420.3040.2980.420.3970.2910.430.3920.344 Log-Gabor0.420.2800.1960.430.4120.2770.440.5280.295 Log of Gaussian0.390.2590.1910.410.3000.2870.420.3840.336

18. 08.06.2007 18Hans Christian Sagbakken Results under blur conditions Blur radius 2Blur radius 4Blur radius 6 TerskelFRRFARTerskelFRRFARTerskelFRRFAR 702-bit Haar wavelet0.300.1260.1180.310.1340.1220.340.2210.180 87-bit Haar wvelet0.370.1810.1280.400.1780.1540.430.2160.210 Log-Gabor0.390.1270.1090.390.1380.1280.410.2630.195 Log of Gaussian0.300.1370.1350.270.1550.1480.250.2510.223

19. 08.06.2007 19Hans Christian Sagbakken Results under light changes Lysintensitet -10%Lysintensitet -5%Lysintensitet +5%Lysintensitet +10% TerskelFRRFARTerskelFRRFARTerskelFRRFARTerskelFRRFAR 702-bit Haar wavelet0.260.2250.1720.290.1750.1590.320.1290.1280.330.1300.113 87-bit Haar wvelet0.370.2430.2070.410.2280.2290.430.2300.2240.430.2560.217 Log-Gabor0.370.2050.1980.390.1680.1270.420.1250.1200.430.1460.122 Log of Gaussian0.310.2120.1760.310.1980.1560.260.2510.2100.330.1530.126

20. 08.06.2007 20Hans Christian Sagbakken Results under rotation 2 grader3 grader4 grader TerskelFRRFARTerskelFRRFARTerskelFRRFAR 702-bit Haar wavelet0.350.2300.2090.320.1790.1510.350.2290.213 87-bit Haar wavelet0.400.1950.2300.400.2690.1870.420.2810.259 Log-Gabor0.400.1480.1390.400.1600.1350.420.2060.174 Log of Gaussian0.340.1780.1470.350.1730.1720.370.2770.232

21. 08.06.2007 21Hans Christian Sagbakken Conclusions Under noisy conditions the best results where achieved with 702-bit Haar wavelet filter Under noisy conditions the best results where achieved with 702-bit Haar wavelet filter Under blur conditions the best results where achieved with 702-bit Haar wavelet filter Under blur conditions the best results where achieved with 702-bit Haar wavelet filter Under light changes the best results where achieved with 702-bit Haar wavelet filter and Log-Gabor filter Under light changes the best results where achieved with 702-bit Haar wavelet filter and Log-Gabor filter Under rotation the best results where achieved with Log-Gabor filter Under rotation the best results where achieved with Log-Gabor filter Totally the best filter is 702-bit Haar wavelet filter Totally the best filter is 702-bit Haar wavelet filter

22. 08.06.2007 22Hans Christian Sagbakken Questions???