Lie Detection

Vinod Reddy (09005071)Bhanu Prakash (09005050)Hasan Kumar (09005065)

by

Introduction Human Lie Detection Techniques Micro-Expression based design Controversy & false-positive results

Outline

Lie detection is the practice of attempting to determine whether someone is lying.

Usually this involves asking the subject control questions where the answers are known to the examiner and comparing them to questions where the answers are not known. 

Introduction

Lie Detectors(though not an accurate name) In event of crime,

◦ Can be used in interrogating ◦ To find the truthfulness of the evidence.

Why is deception detection important?

May prove useful when hiring potential employees employee theft revealing whether or not your future

spouse/girlfriend truly loves you or is after your money

Dealing with your stock broker, sales rep, lawyer, ex-wife, car dealer, mechanic, scam artist, etc.

Why is deception detection important?

To detect whether a person is lying, it is important to know what to look for in the person which shows he is lying.

For this we need to know when a lie fails.

Lie Detection Techniques

Two reasons. Failed to adequately prepare a lie. - lack of adequate thinking Interference of emotions

- lack of control on emotions

WHEN DOES A LIE FAIL ?

Two reasons. Failed to adequately prepare a lie. - lack of adequate thinking Interference of emotions

- lack of control on emotions

WHEN DOES A LIE FAIL ?

Lies often fail because of inadequate preparation

When liar comes up with a lie at the spot May contradict himself Being caught off gaurd when asked

questions which the liar didn’t anticipate.

Inadequate Preperation

Lies also betrayed by signs of emotions Simplest case is when the liar fabricate

convincingly an emotion which is not felt. Involves concealing his own emotion. Two types of failures 1)some sign of emotion is revealed 2)the liar may produce some

inadvertently a deception cuewhich shows person is

lying.

Interference Of Emotions

How do we usually guess whether the other person is saying the truth?

Based on the behavior of the person

Eye Patterns Cadence of Speech Body Language of a Liar Emotional Gestures

Lie Detection Techniques ( human )

Human lie detection capabilities are limited. For example, a meta-analysis of 253

studies of people distinguishing truths from lies revealed overall accuracy was just 53 percent - not much better than flipping a coin.

Human - conclusion

Simple Lie DetectorBuild one home easily

A polygraph is an instrument that simultaneously records changes in physiological processes such as heartbeat, blood pressure, respiration and electrical resistance (galvanic skin response or GSR)

The polygraph was invented in 1921 by John Augustus Larson, a medical student at the University of California at Berkeley and a police officer of the Berkeley Police Department in Berkeley, California

The underlying theory of the polygraph is that when people lie they also get measurably nervous about lying. The heartbeat increases, blood pressure goes up, breathing rhythms change, perspiration increases, etc. 

Polygraph

 A baseline for these physiological characteristics is established by asking the subject questions whose answers the investigator knows. Deviation from the baseline for truthfulness is taken as sign of lying.

Polygraph

There are three basic approaches to the polygraph test :-The Control Question Test (CQT): compares

physiological response to relevant questions about the crime with the response to questions relating to possible prior misdeeds

The Directed Lie Test (DLT): detect lying by comparing physiological responses when the subject is told to deliberately lie to responses when they tell the truth

The Guilty Knowledge Test (GKT): compares physiological responses to multiple-choice type questions about the crime, one choice of which contains information only the crime investigators and the criminal would know about

Polygraph

Validity : Polygraphy has little credibility among scientists. A 1997 survey of 421 psychologists estimated the test's average accuracy at about 61%, a little better than chance. Critics also argue that even given high estimates of the polygraph's accuracy a significant number of subjects (e.g. 10% given a 90% accuracy) will appear to be lying, and would unfairly suffer the consequences of "failing" the polygraph 

Polygraph

Functional magnetic resonance imaging or functional MRI (fMRI) is an MRI procedure that measures brain activity by detecting associated changes in blood flow

Studies using fMRI have shown that it has potential to be used as a method of lie detection. While a polygraph detects changes in activity in the peripheral nervous system, fMRI has the potential to catch the lie at the ‘source’.

fMRI

The procedure is similar to MRI but uses the change in magnetization between oxygen-rich and oxygen-poor blood as its basic measure

This measure is frequently corrupted by noise from various sources and hence statistical procedures are used to extract the underlying signal

fMRI

The resulting brain activation can be presented graphically by color-coding the strength of activation across the brain or the specific region studied

Using this method, studies have shown that lies can be distinguished 78% of the time

fMRI

Electroencephalography (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain

Brain fingerprinting uses EEG to determine if an image is familiar to the subject. This could detect deception indirectly but is not a technique for lie detecting

Brain Observations

Cognitive chronometry, or the measurement of the time taken to perform mental operations, can be used to distinguish lying from truth-telling

Brain-reading uses fMRI and the multiple voxels activated in the brain evoked by a stimulus to determine what the brain has detected

Brain Observations

Truth drugs such as sodium thiopental and marijuana (historically speaking) are used for the purposes of obtaining accurate information from an unwilling subject

 Information obtained by publicly disclosed truth drugs has been shown to be highly unreliable, with subjects apparently freely mixing fact and fantasy

Drugs

Non-invasive lie detection using non-verbal behaviour is performed by the Silent Talker Lie Detector

It observes and analyses non-verbal behaviour in the form of micro-gestures while a subject is being interviewed

It is grounded in the psychological theory that non-verbal behaviour is modified by a number of influences when a person is being deceptive. These include arousal (in particular stress), cognitive load, duping delight, and behaviour control

Non-Verbal Behaviour

At the University of Texas at Austin, psychology professor James Pennebaker, PhD, and his associates have developed computer software, known as Linguistic Inquiry and Word Count (LIWC), that analyzes written content and can, with some accuracy, predict whether someone is lying. Pennebaker says deception appears to carry three primary written markers:

Fewer first-person pronouns. Liars avoid statements of ownership, distance themselves from their stories and avoid taking responsibility for their behavior, he says.

More negative emotion words, such as hate, worthless and sad. Liars, notes Pennebaker, are generally more anxious and sometimes feel guilty.

Fewer exclusionary words, such as except, but or nor--words that indicate that writers distinguish what they did from what they did not do. Liars seem to have a problem with this complexity, and it shows in their writing.

Linguistic Inquiry and Word Count (LIWC)

Need for it ? ◦ Standard polygraph – easily faked◦ Not counted as evidence in courts

Differences from standard polygraph◦ Micro-gestures◦ Automated◦ No physical contact needed◦ No trained psycho-physiologist required

Lie Detector Design with fuzzy neural networks(FNN)

Building Blocks An Artificial Neural Network (ANN)

Camera

System Flow Graph

Working

Pretest Feed data to the system

◦ Case to be investigated◦ Date and time of the crime◦ Details of the crime◦ Subject’s social background, medical history and

criminal record (with cooperation of subject)

Data is now scanned and checks for certain vindictive words◦ e.g. robbed, murder, jail◦ pattern matching techniques

Higher total number of incriminating words found, closer the value of I1 to 1.◦ I1 = fraction of such words found to total such

words recognized by system.

Working

Pretest Input Variable

Also known as relevant-irrelevant test. ◦ Relevant questions – real issue of concern to investigatione.g. asking who did it, about evidence, etc.

◦ Irrelevant questions – provoke no emotion

Irrelevant questions are typically asked first.◦ Physiological response of no diagnostic value

Guilty◦ Stronger reaction to relevant questions

Innocent◦ React similarly

Working

General test

Convert receiving operator characteristic (ROC) curve/graph (analog signal) to digital signal.

Difference of consecutive peaks and lows is taken and averaged out over total number of such differences to give I1i i.e., input variable for the ith response for the general test.

WorkingGeneral test Input variables

Comparison question test Ask about general undesirable acts. Peak-of-tension test

◦ Questions are asked in an easily recognized order.◦ A guilty examinee

Responsiveness increases as correct alternative approaches in question sequence

Decreases when it has passed Others

◦ e.g. probable-lie and directed-lie comparison tests, known-solution peak-of-tension test

Working

Control Test

Convert receiving operator characteristic (ROC) curve/graph to digital signal.

Difference of consec. peaks and lows is averaged out to give I2i i.e., input variable for the ith response in the control test

If there are n physiological parameters,◦ Then #input Variables = (2n+1) ( n – general test, n – control test and 1 –pretest)

Input variables are fed into neural network (trained beforehand) to generate output.

Working

Control Test Input Variable

Fuzzy vs crisp neural network◦ Membership functions vs weights

Obtaining the data set ◦ (membership functions of each of the input variables)

# data regions = # cases used for training

Generating Membership Function

Similar to Feed Forward Crisp Neural Network.◦ Sigmoid neuron

Training

# output in neural network = n+1 Today’s lie detectors, responses in the form

of graph. Fuzzy mathematical expressions must be

brought to deal with such situations. (n+1) membership functions combine to

give a unique membership function outside neural network, which in turn must be defuzzified to give final output◦ Min of all membership functions – benefit of doubt

to the examinee

Output

Mathematically, de-fuzzification of a fuzzy set is a process of rounding it off from its location in the unit hypercube to the nearest vertex.

Put simplyFor our system, we propose the value of λ = 0.5; i.e. for any output greater than 0.5 the output would be 1, otherwise 0.  In this case, the case 1 would mean the person is a liar, while 0 would mean the person is truthful.

De-fuzzifying final output

Difficult to spot and analyze manually. Requires high processing powers to capture

and analyze micro-gestures.

Drawbacks of using micro-expressions

prepare yourself in advance by thinking about what confessions they are looking for, that you can know what things to admit and what things to deny.

Fooling the detectors

What did we discuss?◦ Lie Detection◦ Human Methods◦ Techniques used◦ Silent Talker Design◦ Controversy

Though Lie Detectors are not completely accepted by the scientific community, the day might not be far away…

Conclusion

Bhattacharjee, Anwesha "An Efficient Lie Detector Using FNN", at the IEEE 7th Student Conference on Research and Development, University Putra Malaysia, 2009. https://sites.google.com/site/fnnliedetector/

Charting the behavioural state of a person using a back propagation neural network, Janet Rothwell, Zuhair Bandar, James O’Shea, David McLean. 2009

Simple Lie Detector - http://www.aaroncake.net/circuits/lie.asp http://sosuave.net/forum/showthread.php?t=166543 Use of Fuzzy Set Classification for Pattern Recognition of

Polygraph. Knapp, Ulka, jacobs, 1995.

References

Thank you