-Sridhar Godavarthy
 A Little Background: Blink
 A Lot More Background: Strain as a Soft
Forensic Evidence




Facial Recognition
Culprits
Human anatomy as a feature
Strain Measurement
 Micro expression Detection using Strain Patterns
 Challenges
 Sample Strain patterns
 References
 A Little Background: Blink
 A Lot More Background: Strain as a Soft
Forensic Evidence




Facial Recognition
Culprits
Human anatomy as a feature
Strain Measurement
 Micro expression Detection using Strain Patterns
 Challenges
 Sample Strain patterns
 References
A Little Background
 Why are some people brilliant decision makers?
 How do some people act upon instincts?
 Why are we unable to explain some decisions?
 Great decision makers are not ones that process the
most information
 Malcolm Gladwell’s ‘The statue that didn’t look right’
 They are those who have perfected the art of “Thin
Slicing”
 Filtering out the very few factors that matter.
 A Little Background: Blink
 A Lot More Background: Strain as a Soft
Forensic Evidence




Facial Recognition
Culprits
Human anatomy as a feature
Strain Measurement
 Micro expression Detection using Strain Patterns
 Challenges
 Sample Strain patterns
 References
A Lot More Background
V.Manohar, D.B.Goldgof, S.Sarkar,Y.Zhang
Some slides have been adapted from the Authors’ presentation
 Face recognition has made huge advances
 Picasa’s Web Albums
 Sony’s “say cheese”( or is it CHEERS) detection
 “Almost” perfect
 Picasa still confuses between closely related faces
 Canon almost always never detects my face
 Some say - might be because of my hair ;-)
 Has anyone used the Lenovo Face ID?
 Because they use static images
 Could be supplemented for better performance.
 Illumination
 Camouflage(Makeup/glasses)
 Facial Hair
 Expressions
 The Solution: Use methods based on Human Anatomy
 Iris scan
 Retina scan
 Skull X-ray
 Disadvantage
 Require Specialized equipment
 Intrusive
 Proposed Alternative
 Skin and tissues of the face
Authentic
Author Slide
 Different materials have different elasticity
 Elasticity can be modeled
stress
Elasticity   
strain
Known
Calculate
 What is Facial Strain?
 Strain on soft tissue when expressions are made.
 Anatomical method
 Uses a pair of frames to measure deformation
 Why Facial Strain?
 As it is a difference, it is independent of all the earlier
mentioned culprits(ICHE)
 ‘Visual Pattern’ is unique to
every face.
 Easily quantifiable by ‘elasticity’
 Hard to measure – non-linear,
inverse equations
 Can be represented by strain
pattern under specific boundary
conditions
 Is unique to a person.
 Contact strain measurement equipment is already
available.
 Cannot be used if we are looking to identify people at a
Casino/Airport
 Did I mention the actual applications of this paper
 Soft forensics based on surveillance videos
 Two major steps
1.
2.
Obtain motion field between two frames
Compute strain image from above Motion field.
 Feature Based
 Need to identify features – Difficult!
 Features may be ill defined( when camouflaged)
 Usually requires manual intervention
 Produces a sparse motion field
 Produce Good correspondence in large motion
 Optical Flow based
 Fully automated
 Dense Motion field.
 Requires constant illumination
Adapted
Author Slide
 Observed motion over sequential image frames
 3D Strain
 Ideal
 No high speed equipment available to capture range
images
 2D Strain
 Well – not much of a choice
 Authors could use existing data.
Authentic
Author Slide
 Variation of displacement values obtained from optical flow
 Calculated by taking the derivative of each pixel

Sobel operator (central difference)
 Finite Element Method
 Forward modeling when Dirichlet condition is satisfied
 Good at handling irregular shapes
 Computationally expensive
 This method is an approximation to the solution
 Finite Difference Method
 Strain, a tensor, can be expressed derivatives of the
displacement vector
 This can be approximated by a Finite Difference Method.
 Very efficient when carried out on a regular grid.
 This method is an approximation to the differential equation
 Finite Strain tensor
 Cauchy tensor
 Motion is mostly vertical
 Strain pattern is dominated by its normal components
 The strain magnitudes are scaled to gray levels
 White = highest strain
 Black = lowest strain
 It is now a pattern matching problem.
 Motion field : Based on Optical flow
 Strain Type: 2-D
 Computation: Finite Difference Method
 Strain Magnitude is now 1-D
 Use PCA to perform matching
 Experiments performed on
 Normal light
 Low light
 Shadow light
 Regular face
 Camouflaged face
 Frontal view
 Profile view
 Neutral expression
 Open mouth
 Subject may not perform the expression to the same
extent every time
 Experiments repeated on shorter, subsampled videos
 Strain measurement seems to be logically correct
 We do not discuss the PCA and hence the recognition
results as they are outside the scope of this discussion.(
But they were good)
 Acts as a supplement to existing recognition methods.
 A Little Background: Blink
 A Lot More Background: Strain as a Soft
Forensic Evidence




Facial Recognition
Culprits
Human anatomy as a feature
Strain Measurement
 Micro expression Detection using Strain Patterns
 Challenges
 Sample Strain patterns
 References
 Macro Expressions:
 Large movement
 Smile
 Talking
 Shaking head
 Micro expressions
 Raising eyebrow
 Fast blinking
 Supplement lie detection
 Very little noise
 As part of a general discussion
 Bond might not have lost even the first time!
c. 1-4
b. 3-4
a. 1-2
1
2
3
4
5
d. 1-3
6
n
e. 4-6
f. 1-6
Frame Strain
a
1-2
100
b
3-4
200
c
1-4
300
d
1-3
200
e
4-6
200
f
1-6
400
Macro Expression
Micro Expression
Noise
 Small movements are inevitable
Solution: Normalize
 Macro expressions also possible
 Eyes always blink. Need to detect changes in speed of
blinking
 Need to identify the frames to be used
 V.manohar, D.B. Goldgof, S.Sarkar, Y. Zhang, "Facial Strain
Pattern as a Soft Forensic Evidence", IEEE Workshop on
Applications of Computer Vision (WACV'07),pp 42-42
 Vasant Manohar, Matthew Shreve, Dmitry Goldgof and
Sudeep Sarkar, "Finite Element Modeling of Facial
Deformation in Videos for Computing Strain Pattern",
International Conference on Pattern Recognition, Dec.
2008
 Matthew A. Shreve, Shaun J. Canavan, Yong Zhang, John R.
Sullins, and Rupali Patil, "Imaging And Characterization
Of Facial Strain In Long Video Sequences",xxxx
 Malcolm Gladwell,” Blink: The Power of Thinking Without
Thinking”, Back Bay Books (April 3, 2007)
Sridhar Godavarthy
Dept. Of Computer Science and Engineering
University of South Florida
sgodavar@cse.usf.edu