presentation gait kjetil-holien
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Gait recognition under non-
standard circumstances
Kjetil Holien
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Disposition
Research questions
Introduction
Gait as a biometric feature
Analysis
Experiment setup
Results
Conclusion
Questions
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Research questions
Main research questions: To what extent is it possible to recognize a person
under different circumstances?
Do the different circumstances have any commonfeatures?
Sub research question: Do people walk in the same way given the same
circumstances?
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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.
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Gait as a biometric feature
Three main approaches: Machine Vision based.
Floor Sensor based.
Wearable Sensor based (our approach).
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Machine Vision
Obtained from the distance
Image/video processing
Unobtrusive
Surveillance and
forensic applications
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Floor Sensor
Sensors on the floor
Ground reaction forces/
heel-to-toe ratio
Unobtrusive
Identification
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Wearable sensors
Sensor attached to the body
Measure acceleration
Signal processing
Unobtrusive
Authentication
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Performances of related work
Body location EER, % Number ofSubjects
Ankle ~ 5 21
Arm ~ 10 30
Hip (our approach) ~ 13 100
Trousers pocket ~ 7.3 50
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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
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Average cycle method
Compute resultant vector:
Time interpolation: every 1/100th sec
Noise reduction: Weighted Moving Average Step detection
Average cycle creation
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Raw data, resultant vector
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Time interpolation and noisereduction
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Step detection (1/2)
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Step detection (2/2)
Consist of several sub-phases: Estimate cycle length
Indicate amplitude details
Detect starting location Detect rest of the steps
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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
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Cycles overlaid
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Average cycle, mean
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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 inthe evening.
Sensor was always at the left hip.
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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.
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Normal walking
EER, %
Automatically Manually1st session 1.64 0.66
2nd session 1.94 1.04
All normal 5.91 4.02
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Other circumstances
EER, %
Automatically Manually
Circle left 2.97 1.31Circle right 5.96 0.90
Fast 3.23 2.94
Slow 10.71 4.80
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All circumstances
Normal vs other circumstances EER between 15-30%
Multi-template 1 template for each circumstance, the others as input
EER = 5.05%
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Common features
Cycle length: Normal: [95..125], average of 109 samples
Fast: [80..110], average of 96 samples
Slow: [110..180], average of 137 samples Circle same as normal
Amplitudes related to cycle length
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Long-term experiment (1/3)
Morning vs morning / evening vs evening Compare sessions at different days intervals
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Long-term experiment (2/3)
Linear regression to compute a linearfunction (y = a + bx).
Use hypothesis testing:
H0: b = 0 (stable walk)
H1: b > 0 (more unstable walk)
Results: Rejected H0 for 90% distance increases as time
passes by.
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Long-term experiment (3/3)
Morning vs evening (same day) andevening vs the consecutive morning No difference in the average scores.
Between 30% and 70% increase compared with 1day interval scores.
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Conclusion
Extremely good EER when comparing thecircumstance with itself.
Different circumstances seems to bedistinct hard to transform X to normal.
Good results when using a multi-templatesolution.
Gait seems to be unstable to some extent need a dynamic template.
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Questions?
Thanks for listening!