CS 194-26 Project 3 [acc id: aez]

Part 1: Correspondences and Mid-way Face

Correspondences: To create the correspondenses, I used ginput together with manually adding in edge points to make sure that the morph accounts for features outside of the main face shape as well (i.e hair).
Midway Face: The 2 faces I used were that of George Clooney and Emma Watson, as shown below.
- computeAffine: Implemented using algebraic method – 6 unknowns, 6 equations
- Midway shape: Generated by averaging the coordinates from image A and B
- Triangulation: computed using Delauney Triangulation on midway shape
- Midway face: Created by looping over triangles of mid-way shape powered by numpy computations (polygon and dot products)
The midway face looks largely good, except slight blur in the left eye. This is largely due to the difference in eye shape and size.

George	Emma	Mid-way Face

morph: Function implemented by re-factoring code used to achieve Part 1, with additional warp_frac and dissolve_frac parameters.
45 frames were taken for different t values from interval [0, 1], resulting in the gif shown below set at 30fps.

Data set: The dataset used is the FEI database of Brazillian faces
- Corresponding points were taken from .pt files
- Images downloaded were in black and white. For the sake of congruency, all images used for computation was black and white too
- For this part, only neutral faces were taken and processed.
Average shape: Computed by taking mean over corresponding points of all the images
Morph face to average: Code largely similar to part 2, re-purposed to end at the average shape.
Average face: Pixels summed and averaged over the average shape.

Extrapolating from the morphing functions used in both Part 2 and 3, I morphed my face to take on more features of the Brazillian face
The most notable change effect I saw was the shrinkage/sharpening of my nose, together with overall broadening of my face.
The caricature also caused an increase in the size of my left eye and significantly reduced the distance between my eyes.
- With regards to the increase in size of eye, one reason could be due to the angle of my original photo which caused my left eye to look larger. Upon caricaturizing, the algorithm might have tried to project the symmetry of the average face and thus overcompensated on the increased size of my eye.
This is overall aligned with how the average face looks like – smaller/defined features of the nose, small gap between eyes, symmetrical face.

Face	Caricature

Oh no, my friend looks so UPSET - time to make him smile.
I did this by taking the average smiling face from the same dataset as in Part 3, and then simply morphed my friend’s face to the average smiling shape of the dataset

Friend	Average smiling

Morphing shape	Morphing appearance	Both (small tuning)	Both (large tuning)