© 2018 IEEE.In recent years, face tracking has become a considerable interest in technology development. Generally, 3D face tracking utilized RGB and Depth Data as its source to detect faces. Thus, it needed to set an environment with ideal ambient for 3D face tracking to work well, or else the face tracking stability rate will degrade. In order to reduce error occurs in 3D face tracking implementation on varying ambient light conditions, this paper specifically deals with an explanation of color component effect analysis upon real-time 3D face tracking using Kinect. The experiment is conducted with projecting 24 different colors into user’s face and analyze the effect of R, G, B, H, S, and V component of each color. This experiment produces data which then analyzed with statistical method ANOVA and regression to determine the most significant component that affect the stability of face tracking with Kinect between R, G, B, H, S, and V. The result shows that face tracking with Kinect has stability above 80% to track face in darkness as the face covered with 91.66% varying colors from common colors palette. With the number of F value equal to 7.765, HSV obtained as the more significant classifier compared with RGB, with Value, or brightness level, as the most significant component that effects the system’s ability to detect faces in a dark room. The result of this research can be used as basic knowledge in choosing animation design background colors for 3D face projection mapping in a nearly complete dark room.

3D face tracking,Animation designs,Color component,Digital Makeup,Face Tracking,Stability analysis,Stability rate,Technology development

3D Face Tracking,Color components,Digital Makeup,HSV,RGB,Virtual Mask

https://doi.org/10.1109/ICSEngT.2018.8606380