large-scale, real-world face recognition in movie trailers week 2-3 alan wright (facial recog....
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Large-Scale, Real-World Face Recognition in Movie Trailers
Week 2-3Alan Wright
(Facial Recog. pictures taken from Enrique Gortez)
Preliminary Steps
• Extract Facial Tracks- Working on MATLAB code now
• Worked on detecting blurry images, no solid results.
• Extract the features from the facial tracks.• Build framework to load and test data.• Begin with baseline testing (Sparse, min, meant,
etc)• Algorithm development…
Blur Detection
• Canny Edge Detection• Hough transform• Hough Lines• Find perpendicular line• Using that perpendicular line, get two parallel
lines on each side of the Hough line. • Choose 10 points on each side to find the
gradient.
Hough Lines
Using Perpendicular lines
Gradient Points
Good Edge
Mean
Inte
nsity
Pixels 1 - 20 (10 on each side of the Hough Line)
Bad Edge
Results
• Bad Hough Lines
• Dataset is not ideal for this algorithm, but works well on larger photos.
Facial Recognition
Linear Combination
+ x2 + x3
+ x4 + x5 + x6
+ x7 + x8 + x9
Test Image
= x1
Training Images
Linear Combination
y
Testing
A=
=
Training
x
Coefficients
Now in videos…
• We have:
Instead of:
Baseline
Sparse Representation-based Classification (SRC)
+ x2 + x3
+ x4 + x5 + x6
+ x7 + x8 + x9
Test Image
= x1
Training Images
+ 0 + 0 + 0
+ 0 + 0 + 0
SRCSparse
Linear
SRC
• Method– Impose sparsity on coefficient vector
• We want to minimize the coefficient sum to enforce sparsity.
(Wright09)
Minimize coef.
Possible Baseline Algorithms
• Sum up the coefficient vector and take: average, min, etc..
• SRC linear combination.• Then creating our own algorithm…
Related Papers Read
• “Face Tracking and Recognition with Visual Constraints in Real-World Videos”– Project Page
• “Large Scale Learning and Recognition of Faces in Web Videos”