Gorazd Vrček, Peter PeerComputer Vision Laboratory

Faculty of Computer and Information Science, University of Ljubljana Ljubljana, Slovenia

Chalkida, June 19 2009

IWSSIP 2009

Verification, biometry, iris?

System architecture

Results

Conclusion

Iris

Segmentation

Normalization

Feature extraction

Iris comparison

Input image? ROI? Problems (noise)? Segmentation goal? Start...

Getting information about the pupil: Pupil edge

Getting information about the pupil: Center

Radius

(1) indexXleft(2) Xz

(3) coarse center(4) indexYbottom(5) indexXright(6) Cz

(7) indexYup(8) Yz

Getting information about the pupil (outer edge):

Image smoothing Image illumination

Outer iris edge points detection Generating iris mask

Based on Dougman’s homogeneous rubber sheet

With the center in the center of the pupil

Gabor filter (2D Gabor wavelet)

Image convolution with it

The phase transformation used to convert the angles into iris template

Comparison of two iris bit templates Considering iris mask

Shift the bits and calculate again Use the minimal Hamming distance

The comparison within the class provides the comparison of seven images of a person among themselves

The comparison between classes provides the comparison of one iris image of a person with one of all other persons

Result: positive/negative Threshold for positive decision is set to

HD≤0.427

value 0.427 gives FAR 0%, FRR 11.584%

Research prototype → good results Comparison with ICE 2006 results (FAR=0.1%):

To improve: segmentation optimization, noise detection

To upgrade: integrate iris capturing sensor

Group FRR [%]Sagem-Iridian 2.31Cambridge 3.29Iritech 3.84

CVL 7.70