face morphing
Nadia Hyder
OVERVIEW
In this project, I produced a morph animation of my face into
someone else’s face, computed the mean of a population of faces, and extrapolated
from a population mean to create a caricature of myself.
MORPHING
For
my first morph, I used a picture of myself and the provided image of George
Clooney. I resized the two images to be the same size, defined a correspondence
between the two images mapping eyes to eyes, mouth to mouth, chin to chin, etc.
for smooth transformations. I then computed the midway face, triangulated the
images by correspondence points using Delaunay triangulation, and finally
warped the images using inverse affine transformations to produce the morphing
sequence.
DEFINING
CORRESPONDENCE
To
morph the 2 images together, I first had to define pairs of corresponding
points (for facial features) on the two images. I began by selecting points
manually using matplotlib.ginput, but this caused
issues in later steps (triangulation). To improve performance, I used dlib’s facial landmark detector which estimates the
location of 68 (x,y) coordinates that map to facial
structures. These are the input images and their correspondences:


I
then found the triangulations of the correspondence points using Delaunay
triangulation.


FINDING
THE MIDWAY FACE
In
order to morph the images together, I had to define an affine warp function to
warp one triangle to another. I used inverse warping, applying the inverse
affine transformation to every coordinate in the second image. I used inverse/
backward warping because otherwise, forward warping produces “holes” when a
pixel lands between 2 other pixels. Inverse warping on the other hand begins
from the destination image and uses interpolation to produce better results.
The
following are the midway faces produced by applying inverse warping. The final
midway face is produced by averaging the two warped images.
Nadia warped 
George warped 
Midway face 



MORPH
SEQUENCE
To
find the final 45frame morph sequence, I warped 45 midway images and
interpolated at every level, then stitched the images into a gif with a frame
rate of 1/30 fps.
THE “MEAN FACE” OF A POPULATION
I
used the Danes dataset to find the “mean face” of Danish women. I did this by
first finding the average shape of all the images, then used my inverse warping
function on the triangularized images to the average
shape, to create the mean face.
Here
is the mean face achieved from morphing:
Here
are a few examples of how the input images were warped in the process of
creating the mean face:
original 
warped 




I
then computed my face warped into the average geometry, and the average face
warped into my geometry:
Nadia warped 
Average warped 
Midway face 



CARICATURES
Next,
I computed my face warped with the Danes average from above to find a
caricature of myself. Changing the alpha level to be greater than 1 or less
than 0 exaggerated my features relative to the Danes average in opposite
directions.
original 
alpha = 1.5 


BELLS AND WHISTLES: GENDER CHANGE
Finally,
I used my image morphing algorithm on my image and the average Indian male
(image taken from google). This was meant to produce a more “masculine” warp.
Here are the results:
Input 
Input 
Shape warp 
Shape + appearance morph 




I
also contributed to a class morph video. Here is my contribution:
That about sums it up! Needless to say, this was an entertaining project