Post on 20-Dec-2015
Project Final PresentationProject 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.
DescriptionDescription
Comparing signals from both sides.The right hand indicates the arousal
of the left hemisphere and the left hand, the right hemisphere.
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.
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.
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.
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”.
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.
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.
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
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
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
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
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.
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:
Preprocessing The SignalsPreprocessing The Signals
LP Filter
8 sec
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
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
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.
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
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
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.
Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognitionPCA – Principal Component Analyze
PCA
PCA
PCA PCA
PCA
Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition
Questionnaire
Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition
Do you like beer?
Yes!
True
PCA
PCA PCA
PCA
Questionnaire
Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition
Do you think you need a diet?
Nope
False
PCA
PCA PCA
PCA
Questionnaire
Analyzing The Signals – Pattern Analyzing The Signals – Pattern RecognitionRecognition
PCA
PCA PCA
PCA
Results
correlated signals
non-correlated signals
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.
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
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600Order 1-TR
0 500 1000 1500 2000 2500-200
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400Order 1-TL
0 500 1000 1500 2000 2500-200
-100
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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
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
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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
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.
THE END