Supervised Local Linear Embedding (SLLE) for facial paralysis image sequence analysis

This paper proposed a novel approach based on Local Linear Embedding (LLE) for modeling and understanding the temporal behaviour from facial paralysis image sequences. LLE is sensitive to scaling, illumination and face pose. A Supervised LLE (SLLE) based on directed Hausdorff distance is proposed for aligning two image sets from the two sides of the face using one generalized embedding space. The embedded low dimensional manifolds represent the asymmetry of the facial motion regardless of the extrinsic facial asymmetry caused by the natural bilateral asymmetry, illumination and shadows. Experimental results demonstrate that our approach is more reliable than frame difference based methods and optical flow based methods without a significant increase in computational complexity.