Comments:"Redefining Open Minds: Miss Korea 2013 Contestants Face Morphing"
URL:http://jbhuang0604.blogspot.com/2013/04/miss-korea-2013-contestants-face.html
These images remind me of a course project in computational photography I did two years ago. I wonder how the "averaged" face of these 20 contestants look like. Thus, I morph all the face images into a mean face shape, then average over the picture values using the code I developed in the course project. Here is the result.
Finally, I can generate a short movie of face morphing from contestant number 1 to 20. We can now see how one face is smoothly transformed into another.
So far, the above visualization figures are qualitative, we may be able to get more insights by quantitative analysis.
Here we visualize the largest 6 eigenfaces. These eigenfaces encodes the variation within the 20 contestants.
Third, by projecting faces onto the eigenspace, we can analyze how these faces are distributed in the eigenspace. We show here the coefficients of the 20 contestants. Note that most of the energies are concentrated on first two eigenvalues.
Thus, we plot the coefficients corresponding to the first two eigenvectors. We can now see how similar the appearance of these contestants are.
Fourth, we can try to construct the affinity matrix which encodes the pairwise similarity. (blue indicates similar and red indicates dissimilar).
By summing up over columns or rows, we get a measure of how a contestant is different from the rest. In the plot below, x-axis denotes the contestant index (from 1- 20). The y-axis indicates how distinct the contestant is. This measure can also be interpreted as the salient object among the 20 contestants using the surround center information divergence.
For example, contestant 1, 2 and 6 are more distinct than the rest of the contestants.
Contestant 1
Contestant 2Contestant 6
Here are the pictures of all contestants.