This page looks at 'kinetic' biometrics, sometimes characterised
as behavioural biometrics.
It covers -
- what are kinetic biometrics
verification - identification on the basis of how
you wield a pen and imaging of the ink on the paper
- your typing style as an identifier
- walking style as an identifier
- your voice is your passport?
- grip and lip-movement based identification
Kinetic biometrics centre on supposedly innate, unique
and stable muscle actions such as the way an individual
walks, talks, types or even grips a tool.
Those so-called behavioural measures have been criticised
as simply too woolly for effective one to one matching,
given concerns that they are
not stable (for example are affected by age or by externals
such as an individual's health or tiredness on a particular
are not unique
are simply to hard to measure in a standard way outside
the laboratory (with for example an unacceptably high
rate of false rejections or matches because of background
noise or poor installation of equipment).
have responded that such technologies are non-intrusive,
are as effective as other biometrics or should be used
for basic screening (for example identifying 'suspects'
requiring detailed examination) rather than verification.
Signature verification (ie comparing a 'new' signature
or signing with previously enrolled reference information)
takes two forms: dynamic signature verification and analysis
of a static signature that provides inferential information
about how the paper was signed. It can be conducted online
Dynamic Signature Verification (DSV) is based on how an
individual signs a document - the mechanics of how the
person wields the pen - rather than scrutiny of the ink
on the paper.
Advocates have claimed that it is the biometric with which
people are most comfortable (because signing a letter,
contract or cheque is common) and that although a forger
might be able to achieve the appearance of someone's signature
it is impossible to duplicate the unique 'how' an individual
signs. Critics have argued that it provides a blurry measure,
with an inappropriate percentage of false rejects and
DSV schemes typically measure speed, pen pressure, stroke
direction, stroke length and points in time when the pen
is lifted from the paper or pad. Some schemes require
the individual to enrol and thereafter sign on a special
digital pad with an inkless pen. Others involve signing
with a standard pen on paper that is placed over such
a pad. More recently there have been trials involving
three-dimensional imaging of the way that the individual
grasps the pen and moves it across the paper in signing,
a spinoff of some of the facial biometric schemes discussed
earlier in this note.
In practice there appears to be substantial variation
in how individuals sign their names or write other text
(particularly affected by age, stress and health). Systems
have encountered difficulties capturing and interpreting
the data. In essence, the mechanics of signing are not
invariant over time and there is uncertainty in matching.
Some signature proponents have accordingly emphasised
static rather than dynamic analysis, examining what an
image of a signature tells about how it was written. That
analysis is in effect an automation of the document forensics
practiced over the past 150 years and discussed elsewhere
on this site.
Typically it uses high-resolution imaging to identify
how ink was laid down on the paper, comparing a reference
signature with a new signature. In practice the technology
does not perform on a real time basis and arguably should
not be regarded as a biometric, with proponents having
sought the biometric label on an opportunistic basis for
marketing or research funding.
DSV systems have reflected marketing to the financial
sector and the research into handwriting recognition that
has resulted in devices such as the Newton, Palm and Tablet
personal computer. Although there are a large number of
patents and systems are commercially available uptake
has disappointed advocates, with lower than expected growth
and - more seriously - the abandonment by major users
of the technology.
Two points of entry into the literature are the 2003 paper
by Diana Kalenova on Personal Identification using
Signature Recognition (PDF)
and 2002 On-line Signature Verification (PDF)
by Anil Jain, Friederike Griess & Scott Connell.
Proposals for 'mouse dynamics' biometrics - verification
based on how a user pushes a personal computer mouse across
the pad - do not appear to have proceeded.
Comments on graphology - a pseudo-science that purports
to identify an individual's character on the basis of
that person's handwriting - are provided elsewhere
on this site.
Keystroke dynamics uses the same principles as dynamic
signature verification, offering a biometric based on
the way an individual types at a keyboard.
In essence, the keystroke or 'typing rhythm' biometric
seeks to provide a signature - ie a unique value - based
on two time-based measures -
time - the time that the individual holds down a specific
time - the time spent between keys
verification being provided through comparison with information
captured during previous enrolment.
Typically development of that reference template involves
involves several sessions where the individual keys a
page or more of text. Claims about its effectiveness differ;
most researchers suggest that it is dependent on a substantial
sample of text rather than merely keying a single sentence
or three words.
It has been criticised as a crude measure that is biased
towards those who can touch type and that is affected
by variations in keyboards or even lighting and seating.
As a behavioral measure it appears to be affected by factors
such as stress and health. Proponents have argued that
it is non-intrusive (indeed that both enrolment and subsequent
identification) may be done covertly and that users have
a higher level of comfort with keyboards than with eye
Developers have taken different approaches, ranging from
special keyboards for perimeter management to use of monitoring
devices attached to standard keyboards or software housed
on a LAN server. there has however been little commercial
uptake and academic interest in keystroke systems has
waned. Starting points for exploring the published research
are Jarma Ilonen's 2003 Keystroke Dynamics (PDF)
and Keystroke Dynamics as a Biometric for Authentication
by Fabian Monrose & Aviel Rubin.
Recognition on the basis of how an individual walks has
attracted interest from defence and other agencies for
remote surveillance, with software being used for to interpret
live video from cctv
(eg of all traffic through an airport concourse) or infrared
recordings of movement in an area under covert surveillance.
The technology essentially involves dynamic mapping of
the changing relationships of points on a body as that
Early work from the late 1980s built on biomechanics studies
that dated from the time of Eadweard Muybridge
and beyond. It centred on the 'stride pattern' of a sideways
silhouette, with a few measurement measurement points
from the hip to feet. More recent research appears to
be encompassing people in the round and seeking to address
the challenge of identification in adverse conditions
(eg at night, amid smoke or at such a distance that the
image quality is very poor).
Three points for exploring the technology are On Gait
As A Biometric: Progress & Prospects (PDF)
by Mark Nixon & John Carter, Jani Ronkkonen's Video-based
Gait Analysis (PDF)
- both from 2003 - and Keith Price's bibliography.
The effectiveness of the technology is affected by the
availability and quality of reference and source data,
computational issues and objectives. Mapping may be inhibited,
for example, if images of people are obscured by others
in a crowd or by architectural features; the latter is
an issue because of the need to see the individual/s in
motion. Variation because of tiredness, age and health
(eg arthritis, a twisted ankle or prosthetic limb), bad
footwear and carrying objects may also degrade confidence
Most published academic research dates from the past five
years and exploration of gait as a biometric appears to
be driven by the military/intelligence sector (for example
with funding under the DARPA Human ID At A Distance programme).
Proponents have claimed some non-military applications.
A notable instance is the suggestion that it would aid
in automated identification of female shoplifters who
falsely claim to be pregnant, expectant mothers having
a different walk to people who have a cache of purloined
jumpers stuffed in their bloomers. As yet such suggestions
don't appear to have wowed the market, arguably because
of concerns about cost effectiveness and reliability.
In 2005 Heikki Ailisto of the VTT Technical Research Centre
in Finland proposed gait recognition as a mechanism for
protecting mobile phones, based on a 'three dimensional
accelerometer' in mobiles, laptops and other carried items.
Sensors embedded in the devices would recognise the owner's
gait and lock down access if an incorrect password was
not provided when the equipment failed to recognise the
The VTT team ambitiously claimed that 'gaitcode' is reliable
in 90% of instances but acknowledged in Identifying
Users of Portable Devices From Gait Pattern With Accelerometers
"To our knowledge there is no research work published
on gait identification using acceleration sensors".
Identification by voice rather than appearance has a long
history in literature (a 1930s Dorothy Sayers novel
for example features a voice-based vault) but automated
identification was speculative until the 1990s. Development
has largely been a spin-off of research into voice recognition
systems, for example dictation software used for creating
wordprocessed documents on personal computers and call
centre software used for handling payments or queries.
Voice biometric systems essentially take two forms - verification
and screening - and are based on variables such as pitch,
dynamics, and waveform. They are one of the least intrusive
schemes and generally lack the negative connotations of
eye scanning, DNA sampling or finger/palm print reading.
Voice recognition for verification typically involves
speaking a previously-enrolled phrase into a microphone,
with a computer then analyses and comparing the two sound
samples. It has primarily been used for perimeter management
(including restrictions on access to corporate LANs) and
for the verification of individuals interacting with payment
or other systems by telephone.
Enrollment usually involves a reference template constructed
by the individual repeatedly speaking a set phrase. Repetition
allows the software to model a value that accommodates
innate variations in speed, volume and intonation whenever
the phrase is spoken by that individual.
Claims about the accuracy of commercial verification systems
vary widely, from reported false accept and false reject
rates of around 2% to rates of 18% or higher. Assessment
of claims is inhibited by the lack of independent large-scale
trials; most systems have been implemented by financial
or other organisations that are reluctant to disclose
details of performance.
systems have featured in Hollywood and science fiction
literature - with computers for example sampling all telephone
traffic to identify a malefactor on the basis of a "voiceprint"
that is supposedly as unique as a fingerprint - but have
received less attention in the published research literature.
It is unclear whether bodies such as the US NSA
are having much success with automated identification
of the sound of callers.
Reasons for caution about vendor and researcher claims
in hardware (the performance of microphones in telephones,
gates and on personal computers differs perceptibly)
performance of communication links (the sound quality
of telephone traffic in parts of the world reflects
the state of the wires and other infrastructure)
individual's health and age
to disguise a voice
effectiveness of tests for liveness, with some verification
schemes for example subverted by playing a recording
of the voiceprint owner
perimeter management systems thus require an additional
mechanism such as a password/PIN or access to a VPN.
Researchers and solutions vendors have promoted a range
of other kinetic biometrics for verification or surveillance.
Proposed tools such as 'grip-based' verification (a value
based on mapping the configuration of an individual's
hand in gripping a joystick and the pressure exerted)
have not emerged from the laboratory and appear unlikely
to win significant acceptance in competition with other
biometrics (eg palmprint and hand geometry devices).
Concerns about the difficulty of consistently capturing
useful information from live/archived cctv
or other video systems regarding faces has led some researchers
to propose identification on the basis of lip movement.
The mechanism maps the movement of lip geographies.
The technology is discussed in Olga Shipilova's 2003 paper
Person Recognition based on Lip Movements (PDF).
For the moment large-scale uptake in the commercial sector
appears unlikely, given problems with data capture and
next part (genes)