Presentation Gait Kjetil-holien

8/4/2019 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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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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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!

Documents

Presentation Gait Kjetil-holien