Presentation of Master’s thesis

1. Presentation of Master’s thesis Gait analysis: Is it possible to learn to walk like someone else? Øyvind Stang

2. 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.

3. 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.

4. Gait cycleJain et al.: ”Biometrics – Personal Identification in Networked Society” (1999)

5. 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.

6. 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.

7. 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.

8. 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:

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. After one attempt, the screen looked e.g. like this:

16. 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.

17. Template A: m=0,089 (5,08 degrees)

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

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

20. Template D: m=0.036 (2.05 degrees)

21. Template E: m=0.075 (4.30 degrees)

22. 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?)

23. 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.

25. 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.

26. 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.

27. 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.