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CS194-26 Project 3: Image Morphing

Christine Zhu

Defining Correspondances

The objective of this project is to implement a face warping / morphing algorithm that will enable a smooth transition from one face to another. The first step in achieving this is defining "correspondances" for each image (key feature points). Below, we have two of our beloved EECS professors at Berkeley as an example:

Triangulation and Midway Transformation

To actually morph the two images, we find a Delaunay triangulation of the mean correspondances / feature points for the two images. We can see Professor Sahai and Professor Ranade's images with this mean triangulation below. To complete a midway morph, we warp each image to the halfway shape / triangulation and then average the pixel color values from each image to get a 1/2 Professor Sahai and 1/2 Professor Ranade.

Morph Sequence

To compute a full morph, we simply replicate the process above but with different interpolating values for image shape and image color. A gif transformation of the entire morph can be seen here.

"Mean Face" of a Population

Using this same technique, we can find interesting population averages. Here we look at the results for population average of Danish male faces from the IMM face database. We have three examples of each face warped to the mean shape of danish male faces in this dataset, and then an image of the average danish male face overall.

Average Danish Male Face

My Face Shape Warp

Danish Face Shape Warp

Cariacture: Extrapolating From the Mean

Before we used a parameter alpha for interpolation of images (e.g. (1-a)*im1 + a*im2). If we increase this parameter to be above 1, we extrapolate from the mean to emphasize specific features, and thus create cariactures. An example of this is seen below:

Bells and Whistles: Transforming Ethnicity

As an extra fun tidbit, I also tried transforming myself into a peruvian version of myself.