Uncertain Facing

by Sihwa Park (University of California, Santa Barbara)

Uncertain Facing is a data-driven, interactive audiovisual installation that aims to represent the uncertainty of data points of which their positions in 3D space are estimated by machine learning techniques. It also tries to raise concerns about the possibility of the unintended use of machine learning with synthetic/fake data.

As a data-driven audiovisual piece, Uncertain Facing consists of three major components:

1) face data including synthetic images of faces, generated by StyleGAN2, a generative adversarial network (GAN) for generating portraits of fake human faces, and their face embeddings obtained from FaceNet, a deep neural network trained for finding 128-dimensional feature vectors from face images,

2) t-SNE (t-distributed Stochastic Neighbor Embedding), a non-linear dimensionality reduction technique for the visualization of high-dimensional datasets, and

3) multimodal data representation based on metaball rendering, an implicit surface modeling technique in computer graphics, and granular sound synthesis.