Iris Recognition

John Daugman There is only one iris recognition algorithm in use The algorithm was developed mainly by John Daugman, PhD, OBE www.CL.cam.ac.uk/users/jgd1000/ It is owned by the company Iridian Technologies

Advantages of Iris Recognition Irises do not change with age – unlike faces Irises do not suffer from scratches, abrasions, grease or dirt – unlike fingerprints Irises do not suffer from distortion – unlike fingerprints

Finding the Iris in the Image It is easy to find the circular boundaries of the iris

Masking The boundaries of the eyelids can be found Eyelashes and specularities (reflections) can be found These areas can be masked out

Gabor Wavelets

Gabor Wavelets Gabor Wavelets filter out structures at different scales and orientations For each scale and orientation there is a pair of odd and even wavelets A scalar product is carried out between the wavelet and the image (just as in the Discrete Fourier Transform) The result is a complex number

Phase Demodulation The complex number is converted to 2 bits The modulus is thrown away because it is sensitive to illumination intensity The phase is converted to 2 bits depending on which quadrant it is in

IrisCodes This process is carried out at a number of points throughout the image. The result is 2048 bits which describe each iris uniquely Two codes from different irises can be compared by finding the number of bits different between them – this is called the Hamming distance This is equivalent to computing an XOR between the two codes. This can be done very quickly To allow for rotation of the iris images the codes can be shifted with respect to each other and the minimum Hamming distance found

Hamming Distance

Binomial Distribution If two codes come from different irises the different bits will be random The number of different bits will obey a binomial distribution with mean 0.5

Identification If two codes come from the same iris the differences will no longer be random The Hamming distance will be less than expected than if the differences were random If the Hamming distance is < 0.33 the chances of the two codes coming from different irises is 1 in 2.9 million So far it has been tried out on 2.3 million people without a single error

More Advantages of IrisCodes IrisCodes are extremely accurate Matching is very fast compared to fingerprints or faces Memory requirments are very low – only 2048 bits per iris

Small target (1 cm) to acquire from a distance (1 m) Disadvantages of the Iris for Identification Small target (1 cm) to acquire from a distance (1 m) Moving target ...within another... on yet another Located behind a curved, wet, reflecting surface Obscured by eyelashes, lenses, reflections Partially occluded by eyelids, often drooping Deforms non-elastically as pupil changes size Illumination should not be visible or bright Some negative (Orwellian) connotations

Fake Iris Attacks

Fake Iris Fourier Spectrum Due to the dot matrix grid the Fourier Spectrum of the fake iris has 4 extra points