From: Ed Switalski <E.Switalski@bnr.co.uk>
Date: Mon, 11 Apr 94 01:40:11 PDT
To: cypherpunks@toad.com
Subject: Fast Personal Recognition
Message-ID: <199404110839.11826@bnsgs200.bnr.co.uk>
MIME-Version: 1.0
Content-Type: text/plain



			Attention all Citizen-Units !!


		    Look straight into Big Brother eyes.



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               University of Cambridge Computer Laboratory


                        SECURITY SEMINAR


SPEAKER:	John Daugman
		University of Cambridge

DATE:		Wednesday 20th April 1994 at 4.15pm

PLACE:		Babbage Lecture Theatre, New Museums Site

TITLE:		VISUAL RECOGNITION OF PERSONS BY 
		FAILURE OF STATISTICAL INDEPENDENCE

Samples from stochastic signals with sufficient complexity need reveal only
very little agreement in order to reject the hypothesis that they arise from
independent sources. The failure of a statistical test of independence can
thereby serve as a basis for recognising signal sources if they possess
enough degrees of freedom. Combinatorial complexity of stochastic detail can 
lead to similarity metrics having binomial type distributions, and this allows
decisions about the identity of signal sources to be made with astronomic
confidence levels. 

I will describe an application of these statistical pattern recognition
principles in a system for biometric personal identification that analyses the
random texture visible at some distance in the iris of a person's eye. There
is little genetic penetrance in the phenotypic description of the iris, beyond
colour, form and physiology. Since its detailed morphogenesis depends on the
initial conditions in the embryonic mesoderm from which it develops, the iris
texture itself is stochastic, if not chaotic. The recognition algorithm 
demodulates the iris texture with complex valued 2D Gabor wavelets, and
coarsely quantises the resulting phasors to build a 256 byte `iris code' whose
entropy is roughly 173 bits. Ergodicity and commensurability facilitate
extremely rapid comparisons of entire iris codes using 32-bit XOR instructions.
Recognition decisions are made by exhaustive database searches at the rate of
about 10,000 persons per second.


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