Computer facial animation still remains a very challenging topic within the computer graphics community. In this paper, a realistic and expressive computer facial animation system is developed by automated learning from Vicon Nexus facial motion capture data. Facial motion data of different emotions collected using Vicon Nexus are processed using dimensionality reduction techniques such as PCA and EMPCA. EMPCA was found to best preserve the originality of the data the most compared with other techniques. Ultimately, the emotions data are mapped to a 3D animated face, which produced results that clearly show the motion of the eyes, eyebrows, and lips. Our approach used data captured from a real speaker, resulting in more natural and lifelike facial animations. This approach can be used for various applications and serve as prototyping tool to automatically generate realistic and expressive facial animation.