The following research is based on the process and content of my graduation project named “Nicely Nicely all the time! ”, the objective is to explain the 3D modeling, 3D photogrammetry and face tracking, which I have applied over the course of my project. Besides this, I intend to intensively discuss the biases and discrepancies of the use of algorithms and data in the processes of utilizing and testing the programs, in addition to the impact of discrimination and prejudice on real-life situations and data.


Later in my research I stumbled across Safiya Umoja Noble’s book ‚algorithms of oppression’. In it, she discusses how „The near-ubiquitous use of algorithmically driven software, both visible and invisible to everyday people, demands a closer inspection of what values are prioritized in such automated decision-making systems.“ and she asks „that the misinformation and mischaracterization should stop“.


I wonder if engineers would stop building biased technologies if they realized that the languages and algorithms within their programs should be equally impartial to all of us.






Faceshift software 

Facial expression scanning


Bias I

In the course of using FaceShift, the first step is to generate an intermediate model by means of scanning the role player’s face. After this, the facial expressions are well-matched. After scanning the role player’s face, I find that the eyes of the primary model were generally bigger than the real-life model him/herself, which means I had to manually reduce the size and depth of the eyes. If I do not do this, the tracking system will identify my eyes as barely closed. In other words, when I open my eyes, the role player’s eyes stored in the program remained partially opened.The images on right reveals the process of adjusting the eye size of my modelled face. The left eye was captured after adjustment.






Bias II

As illustrated here, in this experiment I use a photograph of myself (Asian), looking from the side, the face model produced through 3D Face Reconstruction is not Asian. At this point, we could say that the computational mode is based on a Caucasian outline or bone structure. In general, Asian don’t have such high brow ridge and nose bridge.







Bias III

In the process of adjusting the model, the MakeHuman fails to represent personified images in good detail even though it is capable of producing 3D avatars of different races. For instance, when creating an Asian avatar, I found that the software could adjust the size of eyes and the depth of sunken eyes in a limited scope, however, the adjustable range of distance between the eyes and the eyebrows were too limited and inadequate. Generally
speaking, eyes of Asians are not so sunken as Caucasians, which is why there appears a wider distance between their eyebrows and eyes. The MakeHuman, however, failed to notice such an issue. As a result, it left much to be desired in terms of adjusting the position and height of the eyebrows: the space between eyes and eyebrows remained quite narrow even though I adjust it to the maximum extent. Accordingly, when creating an Asian figure, it is easy to see a weird face characterizing Asian’s facial features transplanted to a Caucasian’s face.






continuing… …