Mobile Sensor-Based Biometrics Using Common Daily Activities

1. Mobile Sensor-Based Biometrics Using Common Daily Activities Ken Yoneda Gary M. Weiss (presenting) Wireless Sensor Data Mining (WISDM) Lab Fordham University

2. Mobile Sensor-based Biometrics • Security is often achieved via passwords, tokens, keys, etc. • Known problems with these (bad passwords, stolen keys) • A better way: mobile biometrics • Almost everyone has a smartphone • Some people have smartwatches • Both devices contain accelerometer and gyroscope sensors • These sensors measure motion • Idea: People move differently so accelerometer and gyroscope sensor data can be used for biometrics

3. Activities for Motion-based Biometrics • Motion-based biometrics typically uses only walking (gait) • Some researchers pick another activity (e.g., finger snapping) • We evaluate a large number (18) of diverse activities • This is a major contribution • We also evaluate 9 sensor combinations across 2 devices • Four individual sensors (accel, gyro on phone, and watch) • Five sensor combinations of sensors • This is a major contribution

4. Identification vs Authentication

5. The18 Evaluated Activities

6. Data Collection and Transformation • Use Android smartphones and smartwatches • Collected 3 minutes of data per user activity • 51 users and 18 activities  45 hours of data • Most class. algorithms don’t handle time-series data • Sliding window approach 10s non-overlapping segments • Each example formed by calculating 43 high level features • Average and standard deviation of x, y, z axis sensor values

7. Classification Algorithms • Experiments use Scikit-learn (Python module) • K Nearest Neighbor • Decision Tree • Random Forest • Experiments use stratified 10-fold cross validation • Random Forest consistently performs best • In this presentation only show RF results

8. Identification Accuracy (%) using Random Forest

9. Majority-Voting Strategy • Results on prior slide based on one 10s test example • Overly restrictive • Our majority voting strategy uses 5 examples (50 sec of data) and votes to assign most predicted person • Yields much better results

10. Identification Accuracy (%) using Random Forest (with Voting)

11. Goal: Continuous Biometrics • User identified by their motion while performing normal daily tasks (unstructured) • We can only approximate this since only 18 activities and even distribution of each • Next set of results merge all 18 activities

12. Identification Accuracy (%) using All 18 Activities (Random Forest, Voting)

13. Summary of Identification Results Best Sensors for identification • Phone and Watch Accelerometer (“Accel”) best • followed closely by “All” four sensors (phone + watch sensors) • Gyroscope generally not as useful as accelerometer Best Individual Activities for identification without voting • Walking and Jogging activities are best • Clapping and typing are good Using All Activities • Can do very well without activity labels (can predict label) • Good step toward continuous biometrics

14. Authentication Experiments • Binary classification problem: “you” or “imposter” • 1 model per user (51 models given 51 users) • “Imposters” in the test set should not be in train set • Main evaluation metric is Equal Error Rate • Balances two types of errors: false acceptance rate and false rejection rate • EER: FAR = FRR (vary probability threshold for classification)

15. Authentication EER (%) without Voting (Random Forest)

16. Authentication EER (%) Using a Single Activity with Voting (Random Forest)

17. Biometric Rankings:Which Activities are Best

18. Conclusions • Both accelerometers and all-4 sensors perform best • Gyroscope generally not as good as accelerometer • Majority-voting strategy using 5 examples effective • Good biometric identification and authentication performance is achievable with voting • Can get performance even if activities not labeled • Walking is most effective biometric trait • Sitting and clapping are also viable biometric traits

19. Acknowledgements • Many WISDM Lab members who assisted with data collection • This is an expansion of earlier WISDM Lab studies • Jennifer Kwapisz (2010) • Andrew Johnston (2015)

20. Additional Slides (if time permits)

21. Comparison of Algorithms Average Identification Accuracy (%) Using “Accel” Sensor

22. Identification Learning Curve Learning Curve for Amount of Training Data per Activity

23. Authentication Learning Curve Learning Curve for Amount of Training Data per Activity

24. Impact of Number of Examples used for Voting Voting Performance by Number of Examples for Identification Accuracy