Gait recognition under non- standard circumstances Kjetil Holien

Disposition Research questions Introduction Gait as a biometric feature Analysis Experiment setup Results Conclusion Questions 1/27

Research questions Main research questions: –To what extent is it possible to recognize a person under different circumstances? –Do the different circumstances have any common features? Sub research question: –Do people walk in the same way given the same circumstances? 2/27

Introduction Authentication can occur in three ways: –Something you know, password or PIN code. –Something you has, key or smartcard. –Something you are, biometrics. Biometrics are divided into: –Physiological: properties that normally do not change, fingerprints and iris. –Behavioral: properties that are learned, such as signature and gait. 3/27

Gait as a biometric feature Three main approaches: –Machine Vision based. –Floor Sensor based. –Wearable Sensor based (our approach). 4/27

Machine Vision Obtained from the distance Image/video processing Unobtrusive Surveillance and forensic applications 5/27

Floor Sensor Sensors on the floor Ground reaction forces/ heel-to-toe ratio Unobtrusive Identification 6/27

Wearable sensors Sensor attached to the body Measure acceleration Signal processing Unobtrusive Authentication 7/27

Performances of related work Body locationEER, %Number of Subjects Ankle~ 521 Arm~ 1030 Hip (our approach) ~ Trousers pocket~ /27

Gait analysis Sensor records acceleration in three directions: –X (horizontal) –Y (vertical) –Z (lateral) Average cycle method: –Detect cycles within a walk. –A cycle consist of a doublestep (left+right). –Average the detected cycles (e.g. mean, median). –Compute distance between average cycles. Euclidian, Manhattan, DTW, derivatitve 9/27

Average cycle method Compute resultant vector: Time interpolation: every 1/100th sec Noise reduction: Weighted Moving Average Step detection Average cycle creation 10/27

Raw data, resultant vector 11/27

Time interpolation and noise reduction 12/27

Step detection (1/2) 13/27

Step detection (2/2) Consist of several sub-phases: –Estimate cycle length –Indicate amplitude details –Detect starting location –Detect rest of the steps 14/27

Creation of average cycle Pre-processing methods : –Normalize to 100 samples –Adjust acceleration –Align maximum points –Normalize amplitude –Skip irregular cycles Create average cycle: –Mean –Median –Trimmed Mean –Dynamic Time Warping 15/27

Cycles overlaid 16/27

Average cycle, mean 17/27

Experiment setup Main experiment: –60 participants, two sessions of collection. –1st session: 6 normal walks, 8 fast and 8 slow. –2nd session: 6 normal walks, 8 circle walks (4 left and 4 right). Sub-experiment: –5 participants walking 40 sessions 2 months. –Each session consisted of 4 walks in the morning and 4 walks in the evening. Sensor was always at the left hip. 18/27

Results Best results when: –Normalize to 100 samples. –Adjust acceleration. –Aligned maximum points. –Removed irregular cycles. –Mean and median average cycle. –Dynamic Time Warping as distance metric. 19/27

Normal walking EER, % AutomaticallyManually 1st session nd session All normal /27

Other circumstances EER, % AutomaticallyManually Circle left Circle right Fast Slow /27

All circumstances Normal vs other circumstances –EER between 15-30% Multi-template –1 template for each circumstance, the others as input –EER = 5.05% 22/27

Common features Cycle length: –Normal: [ ], average of 109 samples –Fast: [ ], average of 96 samples –Slow: [ ], average of 137 samples –Circle same as normal Amplitudes related to cycle length 23/27

Long-term experiment (1/3) Morning vs morning / evening vs evening –Compare sessions at different days intervals 24/27

Long-term experiment (2/3) Linear regression to compute a linear function (y = a + bx). Use hypothesis testing: –H 0 : b = 0 (stable walk) –H 1 : b > 0 (more unstable walk) Results: –Rejected H 0 for 90%  distance increases as time passes by. 25/27

Long-term experiment (3/3) Morning vs evening (same day) and evening vs the consecutive morning –No difference in the average scores. –Between 30% and 70% increase compared with 1 day interval scores. 26/27

Conclusion Extremely good EER when comparing the circumstance with itself. Different circumstances seems to be distinct  hard to transform X to normal. Good results when using a multi-template solution. Gait seems to be unstable to some extent  need a dynamic template. 27/27

Questions? Thanks for listening!