On the Use of Brainprints as Passwords - AFCEA · On the Use of Brainprints as Passwords Zhanpeng...

On the Use of Brainprints as

Passwords

Zhanpeng Jin

Department of Electrical and Computer Engineering

Department of Biomedical Engineering

Binghamton University, State University of New York (SUNY)

9/24/2015 2015 Global Identity Summit (GIS) 1

Outline

• Introduction

• Methods

• Supervised machine learning approach

• Similarity-based pattern matching approach

• Unsupervised feature learning approach

• Multi-stimulus, multi-channel fusion

• Datasets and Results

• Ongoing work

• Conclusions


Why Brainwaves?

• Existing biometric methods

• Unique physiological and behavior features to identify individuals

• E.g., fingerprint, palm, iris, face and voice

• Problems and limitations

• Duplicable and noncancelable

• Accidental and intentional disclosure

• Not safe enough for high security agencies

• Safety-threatening to the users


Recent Biometrics Breaches


Why Brainwaves?

• Electroencephalograph (EEG)

• Representing brain’s electrical activity by

measuring the voltage fluctuations on the

scalp surface

• Advantages

• Safety for the user, not only for the system

• Practical solution to duress

• Quantify the uniqueness of our cognition

• Non-volitional EEG brainwaves

• Unique memory and knowledge by the user

• Intuitive response not controlled by the user

Time-locked to what?


• Brain response to a stimulus

• Calculation

• Time-locked average

0 100 200 300 400 500 600 700 800 900 1000 1100-50

0

50Raw EEG segments (Sub:1, Ch:Oz, Sti:BW food)

0 100 200 300 400 500 600 700 800 900 1000 1100-50

0

50

am

plit

ude (

uV

)

0 100 200 300 400 500 600 700 800 900 1000 1100-40

-20

0

20

time (ms)

0 100 200 300 400 500 600 700 800 900 1000 1100

-15

-10

-5

0

5

10

15ERP of 36 trails (Ch:Oz, Sti:BW food)

time (ms)

ampl

itude

(uV

)

Sub 1

Sub 13

Sub 29

0 100 200 300 400 500 600 700 800 900 1000 1100-10

0

10

ERPs of Sub 1 (Ch:Oz, Sti:BW food, Trails:35)

ERP 1

ERP 2

0 100 200 300 400 500 600 700 800 900 1000 1100

-10

0

10

ERPs of Sub 13 (Ch:Oz, Sti:BW food, Trails:35)

am

plit

ude (

uV

)

0 100 200 300 400 500 600 700 800 900 1000 1100-5

0

5

10ERPs of Sub 29 (Ch:Oz, Sti:BW food, Trails:35)

time (ms)


Event-Related Potential (ERP)

• Feature extraction

• Wavelet package decomposition

• Subbands

• Delta: 0-4 Hz

• Theta: 4-8 Hz

• Alpha: 8-15 Hz

• Beta: 15-30 Hz

• Gamma: 30-60 Hz

• Features:

• Mean

• Standard deviation

• Entropy

• Neural network

• Hidden layer: 5-60 neurons


Supervised Learning Approach

Data Acquisition and Results

• Sampling • 500 Hz, 1.1 seconds

• Subjects • 32 adult participants: 11 females,

age range 18-25, mean age 19.12

• Channel • Oz

• Stimuli • Acronyms: e.g. MTV, TNT

• Presentation • 2

• ERP • 50 trails average


Pattern Similarity Approach

• Euclidean Distance (ED) • Measures the distance between two time series by aligning the n-th

point of one time series with the n-th point of the other one

• Dynamic Time Warping (DTW) • Finds the optimal alignment between two time series even they are out

of phase according to the time

Fast DTW


Data Acquisition

• Sampling

• 500 Hz, 1.1 seconds

• Subjects

• 30 adult participants: 14

females, age range 18-25,

mean age 19.53

• Channels

• Pz, O1, O2, O4

• Presentation

• 2

• ERP: 50 trails average

• Stimuli

• Words: e.g., BAG, FISH

• Pseudo words: e.g., MOG, TRAT

• Acronyms: e.g. MTV, TNT

• Illegal strings: e.g. BPW, PPS


Results

• Channel Oz shows stronger

distinguishing capability

• The occipital region seems

to be a best location to

reflect the brain response to

visual stimuli

• Brain responses are more

distinguishable to unfamiliar

or well understood stimuli

• Illegal strings and words

have higher accuracy than

acronyms and pseudo words

Channel

Stimuli Pz O1 O2 Oz

Acronyms 53.33% 58.17% 57.83% 67.83%

Illegal Strings 72.00% 71.17% 72.50% 81.17%

Words 68.67% 70.33% 70.17% 78.00%

Pseudo words 57.50% 61.83% 64.17% 68.83%

Channel

Stimuli Pz O1 O2 Oz

Acronyms 33.83% 45.67% 42.00% 55.67%

Illegal Strings 47.00% 43.67% 46.17% 67.17%

Words 49.33% 47.50% 49.17% 62.83%

Pseudo words 36.50% 43.50% 42.67% 49.33%

Results of ED

Results of fast DTW


• Sparse Autoencoder

• Set the outputs equal to the inputs

• Softmax Classifier

• Generalize logistic regression to classification problems

• Semi-supervised Learning

• Sparse Autoencoder + Softmax

𝐽𝑠𝑝𝑎𝑟𝑠𝑒 𝑊, 𝑏 = 𝐽 𝑊, 𝑏 + 𝛽 𝐾𝐿(𝜌||𝜌 𝑗)

𝑠2

𝑗=1

𝑝 𝑦 𝑖 = 𝑗 ℎ 𝑖 ; 𝜃 =𝑒𝜃𝑗

𝑇ℎ(𝑖)

𝑒𝜃𝑙𝑇ℎ(𝑖)𝑘

𝑙=1


Unsupervised Feature Learning

Convolutional Neural Network (CNN) • First proposed by LeCun in 1998, called LeNets*

+ + Softmax


Data Acquisition

• Sampling

• 500 Hz, 1.1 seconds

• Subjects

• 29 adult participants: 14

females, age range 18-43,

mean age 20.69

• Channels

• 30

• Presentation

• 1

• ERP

• 25 trails average

• Stimuli (8 categories): – BW text

– BW Gabor

– BW celeb

– color targets

– BW food

– color food

– hamburger

– passthought


BW Text

• GRE words

• 100 words

• Good results with previous

experiment

• Low frequency words

• Not everyone has meaning

for every subject


BW Gabor Patches

• 100 randomly generated


BW Celebrities and Foods

• Norming for most loved and hated

• 10 celebrities and foods chosen

• 10 items of each

• 100 celebrities

• 100 foods


Color Targets

• Press a button when you see color

75% 25%


BW Food

• 90 food items

Color Food


Results • Low accuracy @(BW gabor)

• High accuracy @(BW celebrities, BW food, and color food)

• Higher accuracy @(Occipital region)

• Accuracy: CNN > SL > CC


Results

• Majority Voting

• Improved the performance of accuracy


Multi-Channel, Multi-Stimulus Fusion


Different stimulus types likely tap into different functional

brain networks – semantic interpretation • Sine gratings: lateral occipital sites

• Color foods: broader region of more anterior scalp sites

• Celebrities: channels intermediate between sine grating and food

areas

Results


Full Combination:

• 6 stimulus types

• 30 channels

Slimmest Combination:

• 4 single-stimulus

classifiers (BW foods,

color foods, color

targets, BW celebrities)

• 1 channel (the middle

occipital (Oz))

Ongoing Work

• Psychological Coercion Attack

• Blackmail-type chronic coercion

• Threat-of-violence-type acute coercion

• Rationale: Forms of coercion that place psychological stress on the

user may cause brain activity to deflect.

• Psychological Entrainment Attack


Conclusions

• Brainprints are a promising and compelling biometric,

particularly for high security scenarios.

• Rooted in unique non-volitional brain responses, associated

with unique memory and knowledge base.

• Cancelable through brainprint recalibrations using different

types of stimulus

• Accurate among individuals and stable over time

• Resistance to coercion, entrainment, and other psychological

attacks

• Challenges in brainwave acquisition and emotional status.


Questions?

9/24/2015 2015 Global Identity Summit (GIS)

Thanks for Listening

More Information

[email protected]

26

This research is supported by NSF and SUNY.

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