Monday, January 16, 2012

Everything is physiological


I apologize to those of you who are not in the biometrics industry, but the hot topic among us the past few weeks has been butt biometrics.


If you haven't heard about this, Jake Kuramoto shared a Bruce Schneier article which linked to a PhysOrg piece.

Cars of the future may use the driver’s rear end as identity protection, through a system developed at Japan’s Advanced Institute of Industrial Technology. A report surfaced earlier this month that researchers there developed a system that can recognize a person by the backside when the person takes a seat.

They are achieving 98 percent accuracy in the lab with this procedure. While the potential is limited - it would be much harder to identify yourself with a rental car, for example - this just offers another way to try to identify people via their physical characteristics.

But I didn't want to talk about butt identification. I wanted to talk about something else.

Because, you see, when this was discussed in the biometrics Yahoo! group, there was an interesting reaction from William Olivadoti, who provided his professional assessment of the Japanese work:

Nahh- That is stone-age biometrics.
It belongs in the Flintstones` Flintmobile.
That is reaching the bottom of the barrel.

Olivadoti went on to say:

Ours is 21st century hi-tech:

Olivadoti's site itself continues the "new biometrics are better than old biometrics" theme:

Did you know Fingerprints were discovered in 1858?

Why are you tied down to 150 year-old 19th Century horse-and-buggy biometrics when you can have the most advanced 21st Century biometric security in the world!

(Incidentally, the use of fingerprints for identification goes back thousands of years, as this piece on Ed German's web site indicates.)

But back to the present. The advanced 21st century method promoted by Olivadoti is "nerve firings," which are described as weak signals which can, using Olivadoti's technology, be detected from up to 7 meters away. But this statement on the website is an interesting one:

The picture and signature are time/date stamped and are sent to a recording PC/dvr/nvr.

The signature is sent then to a dedicated computer that stores the signature and matches it against a database of signatures.

The accuracy is up to 99.999% using mathematical software and/or inexpensive off-the-shelf commercial noise or sound or voice biometric recognition software.

While remembering that "up to 99.999%" technically includes "0%," that is certainly a claim that makes people take notice.

Of course, the biometric nay-sayers of the world (such as Simon Cole) will ask for scientific proof that these nerve firings provide unique ways to identify people. We are directed to a 1999 article (20th century?!?) in the Journal of Neurophysiology, entitled The Neuromuscular Transform: The Dynamic, Nonlinear Link Between Motor Neuron Firing Patterns and Muscle Contraction in Rhythmic Behaviors. Vladimir Brezina, Irina V. Orekhova, and Klaudiusz R. Weiss presented the following abstract:

The nervous system issues motor commands to muscles to generate behavior. All such commands must, however, pass through a filter that we call here the neuromuscular transform (NMT). The NMT transforms patterns of motor neuron firing to muscle contractions. This work is motivated by the fact that the NMT is far from being a straightforward, transparent link between motor neuron and muscle. The NMT is a dynamic, nonlinear, and modifiable filter. Consequently motor neuron firing translates to muscle contraction in a complex way. This complexity must be taken into account by the nervous system when issuing its motor commands, as well as by us when assessing their significance. This is the first of three papers in which we consider the properties and the functional role of the NMT. Physiologically, the motor neuron–muscle link comprises multiple steps of presynaptic and postsynaptic Ca2+ elevation, transmitter release, and activation of the contractile machinery. The NMT formalizes all these into an overall input-output relation between patterns of motor neuron firing and shapes of muscle contractions. We develop here an analytic framework, essentially an elementary dynamical systems approach, with which we can study the global properties of the transformation. We analyze the principles that determine how different firing patterns are transformed to contractions, and different parameters of the former to parameters of the latter. The key properties of the NMT are its nonlinearity and its time dependence, relative to the time scale of the firing pattern. We then discuss issues of neuromuscular prediction, control, and coding. Does the firing pattern contain a code by means of which particular parameters of motor neuron firing control particular parameters of muscle contraction? What information must the motor neuron, and the nervous system generally, have about the periphery to be able to control it effectively? We focus here particularly on cyclical, rhythmic contractions which reveal the principles particularly clearly. Where possible, we illustrate the principles in an experimentally advantageous model system, the accessory radula closer (ARC)–opener neuromuscular system of Aplysia. In the following papers, we use the framework developed here to examine how the properties of the NMT govern functional performance in different rhythmic behaviors that the nervous system may command.

As you can see, this particular paper does not address the issue of unique identification, but certainly the technology can detect that SOMEONE is there.

Olivadoti has apparently spent much of the 21st century providing this technology - I found an October 2003 listing that discussed an "intruder bioelectric signature."

Regardless of whether or not this particular technology can UNIQUELY identify an individual, the fact remains that a lot of information can be gathered from us. Fingers, palms, feet, faces, irises, retinas, veins, voices, DNA, hair, saliva, skin, butt shapes, "nerve firings" - we're all providing information about ourselves all the time.
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