On the Use of Brainprints as Passwords
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1 9/24/ Global Identity Summit (GIS) 1 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)
2 9/24/ Global Identity Summit (GIS) 2 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
3 9/24/ Global Identity Summit (GIS) 3 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
4 9/24/ Global Identity Summit (GIS) 4 Recent Biometrics Breaches
5 9/24/ Global Identity Summit (GIS) 5 Why Brainwaves? Electroencephalograph (EEG) Representing brain s electrical activity by measuring the voltage fluctuations on the scalp surface Time-locked to what? 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
6 amplitude (uv) amplitude (uv) amplitude (uv) 9/24/ Global Identity Summit (GIS) 6 Event-Related Potential (ERP) Brain response to a stimulus Calculation Time-locked average 50 Raw EEG segments (Sub:1, Ch:Oz, Sti:BW food) 0 ERP of 36 trails (Ch:Oz, Sti:BW food) Sub 1 Sub 13 Sub ERPs of Sub 1 (Ch:Oz, Sti:BW food, Trails:35) ERP 1 ERP time (ms) time (ms) ERPs of Sub 13 (Ch:Oz, Sti:BW food, Trails:35) ERPs of Sub 29 (Ch:Oz, Sti:BW food, Trails:35) time (ms)
7 9/24/ Global Identity Summit (GIS) 7 Supervised Learning Approach Feature extraction Wavelet package decomposition Subbands Delta: 0-4 Hz Theta: 4-8 Hz Alpha: 8-15 Hz Beta: Hz Gamma: Hz Features: Mean Standard deviation Entropy Neural network Hidden layer: 5-60 neurons
8 9/24/ Global Identity Summit (GIS) 8 Data Acquisition and Results Sampling 500 Hz, 1.1 seconds Subjects 32 adult participants: 11 females, age range 18-25, mean age Channel Oz Stimuli Acronyms: e.g. MTV, TNT Presentation 2 ERP 50 trails average
9 9/24/ Global Identity Summit (GIS) 9 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
10 9/24/ Global Identity Summit (GIS) 10 Data Acquisition Sampling 500 Hz, 1.1 seconds Subjects 30 adult participants: 14 females, age range 18-25, mean age 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
11 9/24/ Global Identity Summit (GIS) 11 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 Results of ED 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% Results of fast DTW 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%
12 9/24/ Global Identity Summit (GIS) 12 Unsupervised Feature Learning Sparse Autoencoder Set the outputs equal to the inputs J sparse W, b = J W, b + β KL(ρ ρ j ) Softmax Classifier Generalize logistic regression to classification problems Semi-supervised Learning Sparse Autoencoder + Softmax s2 j=1 T p y i = j h i ; θ = eθ j h (i) k l=1 e θ l T h (i)
13 9/24/ Global Identity Summit (GIS) 13 Convolutional Neural Network (CNN) First proposed by LeCun in 1998, called LeNets* + + Softmax
14 9/24/ Global Identity Summit (GIS) 14 Data Acquisition Sampling 500 Hz, 1.1 seconds Subjects 29 adult participants: 14 females, age range 18-43, mean age 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
15 9/24/ Global Identity Summit (GIS) 15 BW Text GRE words 100 words Good results with previous experiment Low frequency words Not everyone has meaning for every subject
16 9/24/ Global Identity Summit (GIS) 16 BW Gabor Patches 100 randomly generated
17 9/24/ Global Identity Summit (GIS) 17 BW Celebrities and Foods Norming for most loved and hated 10 celebrities and foods chosen 10 items of each 100 celebrities 100 foods
18 9/24/ Global Identity Summit (GIS) 18 Color Targets Press a button when you see color 75% 25%
19 9/24/ Global Identity Summit (GIS) 19 BW Color Food Food 90 food items
20 9/24/ Global Identity Summit (GIS) 20 Results Low gabor) High celebrities, BW food, and color food) Higher region) Accuracy: CNN > SL > CC
21 9/24/ Global Identity Summit (GIS) 21 Results Majority Voting Improved the performance of accuracy
22 9/24/ Global Identity Summit (GIS) 22 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
23 9/24/ Global Identity Summit (GIS) 23 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))
24 9/24/ Global Identity Summit (GIS) 24 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
25 9/24/ Global Identity Summit (GIS) 25 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.
26 9/24/ Global Identity Summit (GIS) 26 Questions? Thanks for Listening More Information This research is supported by NSF and SUNY.
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