Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange...

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Project Final Project Final Presentation Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny
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Page 1: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Project Final PresentationProject Final Presentation

Enhanced Lie Detector

By: Lander Shiran and Balaban Nir

Supervisor: Lange Danny

Page 2: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Project GoalsProject Goals

Characterizing signals from the human body, and decides whether the person told the truth.

The project is an extension of the regular lie detector but it uses other methods for decision.

Page 3: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

DescriptionDescription

Comparing signals from both sides.The right hand indicates the arousal

of the left hemisphere and the left hand, the right hemisphere.

Page 4: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Theoretical Overview – Left and Theoretical Overview – Left and Right Brain SplitRight Brain SplitThe brain is divided into two parts:

the left and the right.In the left part of the brain, logical,

verbal and sequential thoughts are processed.

The right part is responsible for emotional, non-verbal thoughts and feelings.

Page 5: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Theoretical OverviewTheoretical Overview

Truth False

• Both lobes are aroused similarly.• The right and the left lobes are working together synchronically.

• The left lobe is aroused and the right is suppressed.• The right lobe is closer to reality and only the left brain can make up new facts – lie.

Page 6: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

The ECG MachineThe ECG Machine

The ECG machine monitors the signal from the subject body to digital signals in the computer, there we can process them as we find fit.

The machine have number of detectors that should be attached to the body in the right places.

Page 7: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

The Questionnaire – Calibration The Questionnaire – Calibration PhasePhaseIn order to recognize a lie, we must

first characterize the truth.

That is why the calibration phase exists: we ask the subject 10 questions and get 10 true answers.

The lie will deviate from the truth “section”.

Page 8: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

The Questionnaire – Examination The Questionnaire – Examination PhasePhase1. The subject will write down the

true answers and keep them for himself.

2. The subject will answer the investigators, while monitored by the ECG machine and will try to “fool” it by telling lies.

3. The subject will give the prewritten answers to the investigators.

Page 9: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

The Questionnaire – GoalsThe Questionnaire – Goals

1. Because the subject writes down the answers, he cannot regret more pressure .

2. We use the practice of “the carrot and the stick” : “anybody who will fool the machine will get a pizza” a motive.

3. We finally get a database of signals that can be organized by true & false.

Page 10: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Measurements – Electrodes Measurements – Electrodes PositionsPositionsThe ideal scenario:

We would like two clean signals from both sides of the body – 2 common are needed.

Common

Channel

Page 11: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Measurements – Electrodes Measurements – Electrodes PositionsPositionsThe symmetric scenario:

The common will be placed in the middle of the body and will create symmetric signals.

Common

Channel

Page 12: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Measurements – Electrodes Measurements – Electrodes PositionsPositionsThe a - symmetric scenario:

The common will be placed on one of the hands so we’ll get one noisy channel and one clean channel.

Common

Channel

Page 13: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Measurements – The SignalsMeasurements – The Signals

The a - symmetric scenario:

At each questionwe will wait for15 sec before presenting the question so thatthe subjectrelaxes.

0 5 10 15 20 25 30-1000

-500

0

500

1000

1500Left hemisphere - noised signal

sec

0 5 10 15 20 25 30-500

0

500

1000

1500Right hemisphere - clean signal

sec

15 sec

Page 14: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Preprocessing The SignalsPreprocessing The Signals

1. Because we have one clean signal and one noisy signal we must filter them in order to compare them.The ECG signal is of greater frequency from the data and therefore we use LPF on both signals (the same delay).

2. Some signals were taken out of the database because the subject was not aroused as expected.

Page 15: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Preprocessing The SignalsPreprocessing The Signals

3. Taking only the appropriate interval, from the 15 sec until the signal fades.In order to achieve that a designated GUI was created:

Page 16: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Preprocessing The SignalsPreprocessing The Signals

LP Filter

8 sec

Page 17: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals - Analyzing The Signals - ParametersParameters Goal – finding parameters that will

indicate whether or not the subject lied.

List of examined parameters: K parameter – indicates the ratio

between the left hemisphere signal and the right one.We calculated the K by :. }{minarg rightkLeftk

k

Page 18: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals - Analyzing The Signals - ParametersParameters

2. Energy difference - in order to find the dominant hemisphere the difference between the two hemispheres energies is calculated:

RightLeftDif

Page 19: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals - Analyzing The Signals - ParametersParameters3. Correlation – assuming that when a subject

tells the truth, his two hemispheres work respectively and when a subject lies, the harmony ceases.

4. Delay between peaks – according to the article, there may be a delay between the reaction of the hemispheres.The delay was calculated according to the peaks.

Page 20: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Analyzing The Signals – Parameters ExamplesParameters ExamplesWe use two methods for analyzing the

parameters:

1. Parameter vs. Question number :

0 5 10 15 20 25 30-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1corr

corr

Page 21: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Analyzing The Signals – Parameters ExamplesParameters ExamplesWe use two methods for analyzing the

parameters:

2. Parameter 1 vs. Parameter 2 :

-1 0 1 2-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1corr vs. k

k

corr

0 10 20 30-1

-0.5

0

0.5

1

1.5k

k

0 10 20 30-1

-0.5

0

0.5

1corr

corr

Page 22: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognitionPCA – Principal Component Analyze

PCA involves a mathematical procedure that transforms a number of correlated variables into a number of uncorrelated variables (not necessarily what we want) called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.

Page 23: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognitionPCA – Principal Component Analyze

PCA

Page 24: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

PCA

PCA PCA

PCA

Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition

Questionnaire

Page 25: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition

Do you like beer?

Yes!

True

PCA

PCA PCA

PCA

Questionnaire

Page 26: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition

Do you think you need a diet?

Nope

False

PCA

PCA PCA

PCA

Questionnaire

Page 27: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition

PCA

PCA PCA

PCA

Results

correlated signals

non-correlated signals

Page 28: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognitionThe first order vector is not expected to

give us much information, but we hope the second or the third order will help us to distinguish between lie and truth.

The PCA algorithm was executed 4 times – True left, True right, False left and False right.

Page 29: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition

1 2 3 4 5 60

0.2

0.4

0.6

0.8 S - TR

1 2 3 4 5 60

0.2

0.4

0.6

0.8 S - TL

1 2 3 4 50

0.2

0.4

0.6

0.8 S - FR

1 2 3 4 50

0.2

0.4

0.6

0.8 S - FL

0 500 1000 1500 2000 2500-400

-200

0

200

400

600Order 1-TR

0 500 1000 1500 2000 2500-200

-100

0

100

200

300

400Order 1-TL

0 500 1000 1500 2000 2500-200

-100

0

100

200

300Order 1-FR

0 500 1000 1500 2000 2500-200

-100

0

100

200Order 1-FL

0 500 1000 1500 2000 2500-4

-3

-2

-1

0

1

2Order 2-TR

0 500 1000 1500 2000 2500-40

-30

-20

-10

0

10

20Order 2-TL

0 500 1000 1500 2000 2500-30

-20

-10

0

10

20

30Order 2-FR

0 500 1000 1500 2000 2500-10

-5

0

5

10

15

20Order 2-FL

0 500 1000 1500 2000 2500-100

-50

0

50

100

150Order 3-TR

0 500 1000 1500 2000 2500-30

-20

-10

0

10

20

30Order 3-TL

0 500 1000 1500 2000 2500-20

-10

0

10

20Order 3-FR

0 500 1000 1500 2000 2500-6

-4

-2

0

2

4

6Order 3-FL

1

2 3

Page 30: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition

0 500 1000 1500 2000 2500-40

-20

0

20

40Order 2-TR

0 500 1000 1500 2000 2500-20

-10

0

10

20Order 2-TL

0 500 1000 1500 2000 2500-6

-4

-2

0

2

4

6Order 2-FR

0 500 1000 1500 2000 2500-3

-2

-1

0

1

2

3Order 2-FL

0 500 1000 1500 2000 2500-4

-3

-2

-1

0

1

2Order 2-TR

0 500 1000 1500 2000 2500-40

-30

-20

-10

0

10

20Order 2-TL

0 500 1000 1500 2000 2500-30

-20

-10

0

10

20

30Order 2-FR

0 500 1000 1500 2000 2500-10

-5

0

5

10

15

20Order 2-FL

0 500 1000 1500 2000 2500-5

0

5

10Order 2-TR

0 500 1000 1500 2000 2500-30

-20

-10

0

10

20

30Order 2-TL

0 500 1000 1500 2000 2500-0.3

-0.2

-0.1

0

0.1

0.2Order 2-FR

0 500 1000 1500 2000 2500-40

-20

0

20

40Order 2-FLSecond orderSecond order

Page 31: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

Analyzing The Signals – Analyzing The Signals – ConclusionsConclusionsAs we can see, the second order of the

PCA can identify lies:in truth, the signals are corresponding to each other while in lie, the signal oppose each other.

Page 32: Project Final Presentation Enhanced Lie Detector By: Lander Shiran and Balaban Nir Supervisor: Lange Danny.

THE END