Exploring bias within the data sets of facial detection and tracking algorithms and the methods involved in training neural networks. Images used in training deep neural networks are commonly marked manually with 68 facial points, which comprise an active shape model, enabling the algorithm to identify each face in the training set. I use the model as a mask, overlaying and obscuring faces, functioning as a signifier for the collective of individuals training facial tracking and detection software.
