CS 180 Project 3

I used portraits of Matt Bomer and Henry Cavill, as shown below. I plotted correspondences for various features of their faces, triangularized them using the Delauney function on the midpoints of each corresponding pairs of points.

To compute the midway face, I performed affine transformations that would transform each corresponding triangle to the midway triangle. More specifically:

1. For each triangle, I found the affine transformation matrix that would transform the midway triangle to the corresponding source images.
2. Then, for each pixel in the triangle, I would interpolate the pixel values in the original images to get the morphed images.
3. Finally, I would take the average of the images to get the morphed image
   

To animate this transformation, I repeated this process for 45 in-between steps, where the middle points would be (1-alpha) * bomer_pts + alpha * cavill_pts, where alpha would vary from 0 to 1 throughout this whole process. Similarly, the result image would be a weighted average, specifically (1-alpha) * morphed_bomer + alpha * morphed_cavill.

I utilized the IMM Face Database, which comprises of 240 images of 40 different people, 7 women and 33 men. Each person has 6 images in various poses/lighting. For this part, I decided to use only the male images with a neutral expression. They were also annotated using 58 landmarks for their eyes, nose, mouth, jaw, and eyebrows. I also appended 4 more landmarks for the corners of the image so the triangulation would cover the entire image and not just the face. Pictured are the original images I used.

I took the mean of all of these points, morphed each image onto the average set of features, and overlayed them all on top of each other.

These are some of the faces mapped onto the mean face. Notice that quite a few of the heads are warped since there are no features for their hairline or upper face.

I also morphed myself into the avg of the dataset, and morphed the average onto my own geometry.

By taking the difference between my geometry and the mean geometry, scaling it by some positive factor, and then adding it back to my original points, I was able to exaggerate some of my features. More specifically, for some alpha > 0, I morphed my face onto the points: my_pts + alpha * (my_pts - mean_pts). The results are shown below.

I performed PCA on a larger subset of the dataset. This time, I included both men and women in the dataset, and used 3 different poses for each person:

• Neutral face, diffuse light
• Happy face, diffuse light
• Neutral face, harsh light on right side of face.
  I used 25 features in total. These were the eigenfaces.
  

Pictured below are a few of the triplets of original images, reconstructed images, and the caricaturized versions of the reconstruction.

Finally, this is a reconstruction of myself. It is much worse, likely due to misalignment and multiple features being different.