In this project, we saw that faces have extremely interesting and universal properties. We explored and implemented techniques to warp images to each other through the deployment of basic Linear Algebra and approximation techniques such as Delauney. It becomes clear that the execution of successful face morphing is not necessarily to be associated with deep learning technologies as many of these technologies have een established in the previous decades.
Defining Correspondence Points
One of the most important things before conducting any morphings is to define correspondence points. This is a way to identify commonalities and constructs the basic structure through which morphing is conducted. Per the project description's discretion, these alignments and correspondences have been picked manually. At first, I attempted to create 70 correspondence points, but after having talked to fellow students, I came to the conclusion that 49 points is enough to produce the exact same result. When conducting this correspondence basis, it is important to remember to pinpoint the 4 corners of the photograph as otherwise the morphing will leave out these areas. Additionally, before constructing any Delauney triangulation, it is important to double check that the correspondence has been put in correctly in sequence. The following correspondence down below has been used throughout the project, altogether with an example of the Delauney triangulation used in creating the Midway Face.
Mean Face of the PopulationThe following task was to create the 'mean face' of a population trying to decipher the average shape as well as the average pixels/colors. The data set used comes from the Technical University of Denmark's Computer Science faculty. It consists of 240 pictures taken from different angles of people that work at their department. The correspondences have been provided in .asf files and thus before use have been parsed and loaded into our python environment. Computing the average was not much different from calculating the midway face or morphing. The only difference here was that we needed to add 1/240 of the pixel values and morph 240 pictures to the average shape. The result down below is quite interesting. The average 'danish Computer Scientist' seems to be a male with mainly Caucasian strengths. In class we talked about how 'oddities' are washed away through taking the mean and hence this average person looking very perfect and symmetrical. Lastly, it also reveals some underlying biases as well, in this situation, showcasing a male rather than a person without leaning towards a certain gender.
The warps have been transformed through manually defining the correspondence of our average Danish Computer Scientist to the basis we created earlier. The center image is the average picture warped to my correspondence. The right picture is vice versa. My asymmetric chin becomes extremely clear. Limitations are that my ears are small and the ears in the average danish Computer Scientist is almost not clear which results in odd stuff going near the ear area.
Quite honestly, I don't have large clue of why my caricature looks like this. This caricature has been generated through calculating the distance of geometry of my correspondence and that of the average 'Danish Computer Scientist'. This difference has been added back to my photo. It appears that my long chin has been exaggerated as well as the asymmetry of the face.