Presentation of Master’s thesis

Gait analysis: Is it possible to learn to walk like someone else?

Øyvind Stang

Introduction

• Definition of biometrics: “The science and technology of measuring and analyzing biological data”. (http://searchsecurity.techtarget.com)

• 2 categories: Behavioural and non-behavioural

• Behavioural: Keystroke, voice, gait.• Non-behavioural: Fingerprints, face, iris.• Impersonation is a well-known problem.

Gait

• 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 person’s 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)

Gait• 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 someone’s gait?

• If it is easy, what does that say about the security of gait authentication?

• Are some people’s gait more difficult to learn than others? => Sheep.

• Are some people better impersonators than others? => 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-value=0.0005, i.e. too little evidence

to support the hypothesis.

Prototype

• 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: 222

iiii ZYXR

Prototype• 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

trivial.• Template C: The author’s natural gait.• Template D: Fast and “shuffling” steps. Difficult.• Template E: Slow, oscillating steps. Difficult.

Prototype• When the program starts, the 4 graphs from one

of the templates are plotted in the coordinate system.

• 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 template’s r-graph, and the user’s r-graph is calculated.

Prototype• 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) (“Pearson’s r”):

• In order to get a score between 0 and 100, the absolute value of the correlation coefficient is multiplied with 100.

22 )()(

))((

bbaa

bbaa

ii

ii

100score

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 it.

• 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 this:

Results

• Linear regression: Finding a linear function, y=mx+b, 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: m=0,089 (5,08 degrees)

Template B: m=0,041 (2,37 degrees)

Template C: m=0,051 (2,90 degrees)

Template D: m=0.036 (2.05 degrees)

Template E: m=0.075 (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 author’s natural gait, it was interesting to see how good he managed to score when trying to walk like himself.

• Template C => 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: 4/1330.8%

B Never: 7/131 time: 2/132 times: 2/135 times: 1/136 times: 1/13

46.2%

C Never: 6/131 times: 3/132 times: 2/133 times: 1/139 times: 1/13

53.8%

D Never: 8/131 time: 4/133 times: 1/13

38.5%

E Never: 10/132 times: 2/135 times: 1/13

23.1%

Conclusion

• It seems rather easy to learn to walk like someone else. Many participants (20%-60%) managed to exceed the author’s 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 algorithms.

• The graphs were not shifted before the correlation was calculated.

Further work

• A bigger experiment with more (natural) templates.

• Involving a camera.• Improved visual interactive feedback.• Sound based feedback.• Difference between different groups.• The issue of wolves and sheep.