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Presentation of Master


Presentation of Master s thesis Gait analysis: Is it possible to learn to walk like someone else? yvind Stang Introduction Definition of biometrics: The ... – PowerPoint PPT presentation

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Title: Presentation of Master

Presentation of Masters thesis
  • Gait analysis Is it possible to learn to walk
    like someone else?
  • Øyvind Stang

  • Definition of biometrics The science and
    technology of measuring and analyzing biological
    data. (http//
  • 2 categories Behavioural and non-behavioural
  • Behavioural Keystroke, voice, gait.
  • Non-behavioural Fingerprints, face, iris.
  • Impersonation is a well-known problem.

  • The gait is a feature that is different from
    person to person.
  • Because of this, it may be used as a biometric.
  • The aim of gait authentication is to look at
    different features in a persons gait, and based
    on these, analyze whether they belong to Person
    X or not.

Gait cycleJain et al. Biometrics Personal
Identification in Networked Society (1999)
  • 3 main categories of gait authentication.
  • Image based gait authentication To use (a)
    camera(s) to capture images of a walking person,
    and then analyzing these images, looking for
    certain features.
  • Floor-sensor based gait authentication.
  • Accelerometer based gait authentication To use a
    sensor containing an accelerometer, which
    measures the acceleration in three directions,
    and then analyze the gait based on this
    acceleration data.

Problem (and relevant questions)
  • How easy or difficult is it to learn to
    impersonate someones gait?
  • If it is easy, what does that say about the
    security of gait authentication?
  • Are some peoples gait more difficult to learn
    than others? gt Sheep.
  • Are some people better impersonators than others?
    gt Wolves.

Previous work
  • Robustness of biometric gait authentication
    against impersonation attack by Davrondzhon
    Gafurov, Einar Snekkenes, and Torkjel Søndrol.
  • Accelerometer based.
  • Distance metric The Cycle Length Method.
  • Their null-hypothesis (H0) Deliberately trying
    to imitate another person will give results.
  • Results p-value0.0005, i.e. too little evidence
    to support the hypothesis.

  • Created a prototype that reads acceleration data
    from a (ZSTAR) sensor.
  • The acceleration data is then plotted in a
    coordinate system as 4 graphs, i.e. the x-graph,
    the y-graph, the z-graph, and the r-graph.
  • The r-graph is the resultant graph, where each
    plot is calculated using the following formula

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  • The prototype reads and plots gait data
    continually in 5 seconds before it stops.
  • Created 5 gait templates of different degrees of
    difficulty (each lasting 5 seconds).
  • Template A Two slow steps. Rather trivial.
  • Template B A few more steps. Also rather
  • Template C The authors natural gait.
  • Template D Fast and shuffling steps.
  • Template E Slow, oscillating steps. Difficult.

  • When the program starts, the 4 graphs from one of
    the templates are plotted in the coordinate
  • When we give instructions to the program to start
    reading the acceleration data, it reads from the
    sensor, and plots the incoming data in the same
    coordinate system.
  • After it has read and plotted in 5 seconds, it
    stops, and the correlation between the templates
    r-graph, and the users r-graph is calculated.

  • A score between 0 and 100 is given, which is
    based on this correlation value.
  • Correlation between 2 datasets A(a1,,an) and
    B(b1,,bn) (Pearsons r)
  • In order to get a score between 0 and 100, the
    absolute value of the correlation coefficient is
    multiplied with 100.

The Experiment
  • On the authentication lab on GUC.
  • 13 participants, all men, but of different weight
    and height.
  • The coordinate system was displayed on a big
    screen, so the participants could see the
    template graphs while they were walking towards
  • They attempted to imitate each template 15 times.

The Experiment
  • The participants did not see the actual gait, but
    were given a simple explanation at the beginning
    of each template.
  • The aim was to see if their scores had a positive
    increase from the beginning (attempt no 1) to the
    end (attempt no 15).
  • The score from each attempt was displayed in a
    pop-up box after the attempt was completed.

After one attempt, the screen looked e.g. like
  • Linear regression Finding a linear function,
    ymxb, that fits to the data.
  • Tells us whether the tendency in data is
    increasing (by having a positive m) or decreasing
    (by having a negative m).
  • We used Linear regression in order to analyze the
    progression from attempt no 1 to attempt no 15.

Template A m0,089 (5,08 degrees)
Template B m0,041 (2,37 degrees)
Template C m0,051 (2,90 degrees)
Template D m0.036 (2.05 degrees)
Template E m0.075 (4.30 degrees)
Analysis of results
  • In all 5 templates, there is a increase in the
    scores from the 1st to the 15th attempt.
  • The increase is not too large.
  • Some participants scored generally high, but had
    a small increase in the scores. (Bad?)
  • Some participants scored generally low, but had a
    large increase in the scores. (Good?)

A new attempt to analyze the results
  • Since Template C contained the authors natural
    gait, it was interesting to see how good he
    managed to score when trying to walk like
  • Template C gt 150 attempts.
  • The median value was 50.73 points, i.e. the
    author scores above 50 points half of the times.
  • How many and how often did the participants
    manage to exceed 50 points?
  • Threshold 50 pts.

Template of times
A Never 9/13 1 time 4/13 30.8
B Never 7/13 1 time 2/13 2 times 2/13 5 times 1/13 6 times 1/13 46.2
C Never 6/13 1 times 3/13 2 times 2/13 3 times 1/13 9 times 1/13 53.8
D Never 8/13 1 time 4/13 3 times 1/13 38.5
E Never 10/13 2 times 2/13 5 times 1/13 23.1
  • It seems rather easy to learn to walk like
    someone else. Many participants (20-60) managed
    to exceed the authors median score.
  • If our conclusion turns out to be true, then gait
    authentication should not be used as the only
    authentication technique.
  • The risk of impersonation will then be too large.

What must be considered?
  • Wolves and sheep?
  • Few participants?
  • Few natural templates?
  • Too little variation between the participants?
  • Other distance metrics (algorithms)? Our
    conclusion is not necessarily true for all
  • The graphs were not shifted before the
    correlation was calculated.

Further work
  • A bigger experiment with more (natural)
  • Involving a camera.
  • Improved visual interactive feedback.
  • Sound based feedback.
  • Difference between different groups.
  • The issue of wolves and sheep.
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