gait recognition under non-standard circumstances
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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: - PowerPoint PPT PresentationTRANSCRIPT
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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 common
features?
• 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 of Subjects
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 noise reduction
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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 in
the evening.
Sensor was always at the left hip.18/27
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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 Manually
1st 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 1.31
Circle 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 linear function (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) and evening vs the consecutive morning– No difference in the average scores.– Between 30% and 70% increase compared with 1 day
interval scores.
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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.
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Questions?
Thanks for listening!