Principal Component Analysis of Two-Dimensional Flow Vector Fields on Human Facial Skin for Efficient Robot Face Design

摘要

In this study, deformation patterns of an adult male lower face are measured and analyzed for efficient face design for android robots. We measured flow vectors for 96 points on the right half of the lower face for 16 deformation patterns, which are selected from Ekman's action units. Namely, we measured 16 flow vector fields of facial skin flow. The flow vectors were created by placing ink markers on the front of the face and then video filming various facial motions. A superimposed image of vector fields shows that each point moves in various directions. Principle component analysis was conducted on the superimposed vectors and the contribution ratio of the first principal component was found to be 86%. This result suggests that each facial point moves almost only in one direction and different deformation patterns are created by different combinations of moving lengths. Based on this observation, replicating various kinds of facial expressions on a robot face might be easy because an actuation mechanism that moves a single facial surface point in one direction can be simple and compact.