Digital Signal Processing Reference
In-Depth Information
and in forensic authentication applications [1-6,18,19]. These include
identification of individuals from their physical features such as fingerprints,
hand geometry, face, retina, and iris.
The second class is classified as behavioral signatures, which include
voice, style of hand-writing, key-stroke dynamics, motion video, gait, lip-
reading, and several others.
Personal identification by digital signatures based on Public Key
Infrastructure (PKI), passwords and smart-cards fall into the class of what we
posses.
Finally, Deoxyribo Nucleic Acid (DNA) is the one-dimensional ultimate
unique code for a person's uniqueness - except for the fact that identical
twins have identical DNA patterns. Together with dental records, it has been
widely used in personal identification mostly for forensic applications. Since
these last two groups do not involve signal processing and they have not
been normally studied in the realm of signal processing. Furthermore, they
have no applicability in vehicular applications.
Traditionally, features used in identification have been extracted from
answers to only one of the three fundamental questions above. Depending on
the application, the performance in terms of accuracy and robustness can
vary between excellent to unacceptable. In particular, the chamber, where the
systems are deployed has been the major deciding factor between the success
and failure. For instance, the systems which give excellent results in a
controlled testing environment have yielded almost all the time unacceptably
poor performance in real-life situations. These include the cockpit, crowded
rooms, shopping centers and, in particular, moving vehicles.
Many practical and even costly signal enhancement procedures have been
resorted to improve the performance without much success, which in turn,
has significantly limited the penetration of biometrics into the realm of e-
transactions, i.e., e-business, m-commerce (business in mobile environment)
and p-commerce (secure transaction over phone.)
Recently, algorithms using the multi-mode sensor approach to biometric
identification have been developed with encouraging results in Chapter 16
and in [10-12]. In particular, the combination of feature sets extracted from
iris, finger and video information [10-12]; the fusion of audio and video
characteristics in Chapter 16 and the resulting improved performance can be
shown as examples in the right direction.
In this paper, we focus on behavioral signals obtained from the driving
characteristics of individuals, namely, the distributions and the spectra of
