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. Author manuscript; available in PMC: 2010 Jul 8.
Published in final edited form as: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2008 Jun 23;2008:1–8. doi: 10.1109/CVPR.2008.4587808

Figure 4.

Figure 4

A comparison of tracking results on a subset of the UNBC-McMaster archive [1] which includes video clips of 6 clinical patients with significant head motion and facial expression. There are 200 – 400 frames in each video sequence. To make this task even more challenging, we trained all models, including the PDM and the patch experts, separately on the MultiPIE face database [10]. The definition of the terms can be found in the caption of Figure 3. As we can see, all CLM methods had much better performance than the holistic AAM method. Furthermore, the proposed CQF and RCQF method outperformed the ELS method by a larger margin in the accuracy and convergence rate compared to Figure 3. One hypothesis is that the patch experts trained in one data set does not perform as well in a new data set. By enforcing the convex constraint, the joint optimization can suppress the outliers and improve the robustness and accuracy of the non-rigid alignment.