Lie Detection System Using
Facial Expressions
NATHAN DE LA CRUZ
SUPERVISOR: MEHRDAD GHAZIASGAR
MENTORS: DANE BROWN AND DIEGO
MUSHFIELDT
Introduction
Background
Research has found:
 More than
80% of women admit to occasionally telling
“harmless half truths”.
 31% of people admit to lying on their CV’s.
 60% of people lie at least once during a 10 minute
conversation and on average tell 2 to 3 lies.
Ways to detect Lies
 Study body language
Ways to detect Lies
 Studying Eye movements
LIE
TRUTH
Ways to detect Lies
 Observing micro-expressions
User Requirements
 The user requires the system to accurately tell if a person is




or is not lying.
The user should be able to initialize the software.
The user will ask the subject a series of questions.
The software should be monitoring the subjects’ response.
At any time the user should be able to stop the processing
and get a result.
Requirements Analysis
What Is Needed?
 A Web Camera
 A PC with Open Computer Vision (OpenCV) libraries
Installed.
Requirements Analysis
Complete Analysis
 Process Initialization (Clicking Some Button)
 Capturing Video In Real Time & Detecting The Face
 Pre-processing Frames
 Processing Frames Using Optical Flow
 Process Termination (Clicking Some Button)
 Displaying Information to User
Requirements Analysis
Capturing Video In Real Time & Detecting The Face
Requirements Analysis
Pre-processing Frames
Greyscale
Cropping
Requirements Analysis
Processing Frames Using Optical Flow
Requirements Analysis
Displaying
Information to
User
•
Either a “Passed” or
a “Failed” message
will be displayed
•
User not faced with
detailed information
•
Improves the user
understandability
aspect of the
software
Project Plan
Goal
Due date
• Learn to use OpenCV functions/tools to manipulate
images and videos
• Requirements Gathering
End of Term1 (Completed)
Design and Development
• Creating User Interface Specification
• Designing structure of code
• Identifying 2 micro-expressions and 2 macro-expression
End of Term2 (Completed)
Implementation
• Training SVM to identify more micro-expressions
• Optimizing LBP by altering the smoothing function
Testing and Evaluating
• Collect more training data for SVM
• Collect more test data for SVM
End of Term3
End of Term4
References
Robert Etherson. (2008). people admit to lying on their resumes. Available:
http://www.databaserecords.com/blog/31-of-people-admit-to-lying-ontheir-resumes/. Last accessed 28th March 2013.
2. Michelle Adler. (2009). little white coat lies. Available:
http://www.newsweek.com/2009/01/07/little-white-coat-lies. Last accessed
28th March 2013.
3. Henry Bach . (2004). read face deciphering micro-expression. Available:
http://www.divinecaroline.com/22189/83672-read-face-decipheringmicroexpressions. Last accessed 28th March 2013.
1.