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section heading icon     kinetics

This page looks at 'kinetic' biometrics, sometimes characterised as behavioural biometrics.

It covers -

  • introduction - what are kinetic biometrics
  • signature verification - identification on the basis of how you wield a pen and imaging of the ink on the paper
  • keystroke - your typing style as an identifier
  • gait - walking style as an identifier
  • voice - your voice is your passport?
  • others - 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

  • are not stable (for example are affected by age or by externals such as an individual's health or tiredness on a particular day),
  • are not unique
  • or 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).

Proponents 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

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 or offline.

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 acceptances.

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 -

  • dwell time - the time that the individual holds down a specific key
  • flight time - the time spent between keys

with 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 scanning.

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 (PDF) 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 person moves.

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 in results.

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 walk.

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 that (PDF) "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.

Screening 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 include -

  • variations in hardware (the performance of microphones in telephones, gates and on personal computers differs perceptibly)
  • the performance of communication links (the sound quality of telephone traffic in parts of the world reflects the state of the wires and other infrastructure)
  • background noise
  • the individual's health and age
  • efforts to disguise a voice
  • the effectiveness of tests for liveness, with some verification schemes for example subverted by playing a recording of the voiceprint owner

Most 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 processing.

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version of October 2005
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