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A universal implementation for most behavioral Biometric systems is still unknown since some behaviors aren't individual
enough for identification. Habitual behaviors which are measurable by sensors are considered 'soft' biometrics (i.e., walking
style, typing rhythm), while physical attributes (i.e., iris, fingerprint) are 'hard' biometrics. Thus, biometrics can aid in the
identification of a human not only in cyberspace but in the world we live in. Hard biometrics have proven to be a rather
successful form of identification, despite a large amount of individual signatures to keep track of. Virtually all soft biometric
strategies, however, share a common pitfall. Instead of the classical pass/fail decision based on the measurements used by hard
biometrics, a confidence threshold is imposed, increasing False Alarm and False Rejection Rates. This unreliability is a major
roadblock for large scale system integration. Common computer security requires users to log-in with a six or more digit PIN
(Personal Identification Number) to access files on the disk. Commercially available Keystroke Dynamics (KD) software can
separately calculate and keep track of the mean and variance for each time travelled between each key (air time), and the time
spent pressing each key (touch time). Despite its apparent utility, KD is not yet a robust, fault-tolerant system. We begin with a
simple question: how could a pianist quickly control so many different finger and wrist movements to play music? What
information, if any, can be gained from analyzing typing behavior over time? Biology has shown us that the separation of arm
and finger motion is due to 3 long nerves in each arm; regulating movement in different parts of the hand. In this paper we wish
to capture the underlying behavioral information of a typist through statistical memory and non-linear dynamics. Our method may reveal an inverse Compressive Sensing mapping; a unique individual signature.
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