Large-Scale, Real-World Face Recognition in Movie Trailers Week 2-3 Alan 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.
Good Edge Intensity Mean Pixels 1 - 20 (10 on each side of the Hough Line)
Results • Bad Hough Lines • Dataset is not ideal for this algorithm, but works well on larger photos.
Linear Combination Training Images Test Image = x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
Linear Combination A x y = = Coefficients Testing Training
Now in videos… • We have:Instead of:
Sparse Representation-based Classification (SRC) Training Images Test Image = x1 + x2 + x3 + x4 + 0 + 0 + x5 + x6 + 0 + x7 + 0 + x8 + 0 + x9 + 0
SRC Sparse Linear
SRC • Method • Impose sparsity on coefficient vector • We want to minimize the coefficient sum to enforce sparsity. Minimize coef. (Wright09)
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”