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

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Presentation on theme: "08.06.2007 1 Iris Recognition Under Various Degradation Models Hans Christian Sagbakken."— Presentation transcript:

1 Iris Recognition Under Various Degradation Models Hans Christian Sagbakken

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

3 Hans Christian Sagbakken Introduction

4 Hans 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 Hans Christian Sagbakken Example of an iris pattern

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

7 Hans Christian Sagbakken Scope and research questions

8 Hans 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 Hans 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 Hans Christian Sagbakken Experimental setup

11 Hans 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 Hans 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 Hans 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 Hans Christian Sagbakken Example of hamming distribution Inter-class comparisonsIntra-class comparisons

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

16 Hans Christian Sagbakken Results

17 Hans Christian Sagbakken Results under noisy conditions Støyvarianse 0.002Støyvarianse 0.004Støyvarianse TerskelFRRFARTerskelFRRFARTerskelFRRFAR 702-bit Haar wavelet bit Haar wavelet Log-Gabor Log of Gaussian

18 Hans Christian Sagbakken Results under blur conditions Blur radius 2Blur radius 4Blur radius 6 TerskelFRRFARTerskelFRRFARTerskelFRRFAR 702-bit Haar wavelet bit Haar wvelet Log-Gabor Log of Gaussian

19 Hans Christian Sagbakken Results under light changes Lysintensitet -10%Lysintensitet -5%Lysintensitet +5%Lysintensitet +10% TerskelFRRFARTerskelFRRFARTerskelFRRFARTerskelFRRFAR 702-bit Haar wavelet bit Haar wvelet Log-Gabor Log of Gaussian

20 Hans Christian Sagbakken Results under rotation 2 grader3 grader4 grader TerskelFRRFARTerskelFRRFARTerskelFRRFAR 702-bit Haar wavelet bit Haar wavelet Log-Gabor Log of Gaussian

21 Hans 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 Hans Christian Sagbakken Questions???


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