Gait recognition under non-standard circumstances

Kjetil Holien

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

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!