  Current face alignment algorithms can robustly find a set of landmarks along
face contour. However, the landmarks are sparse and lack curve details,
especially in chin and cheek areas where a lot of concave-convex bending
information exists. In this paper, we propose a local to global seam cutting
and integrating algorithm (L2GSCI) to extract continuous and accurate face
contour. Our method works in three steps with the help of a rough initial
curve. First, we sample small and overlapped squares along the initial curve.
Second, the seam cutting part of L2GSCI extracts a local seam in each square
region. Finally, the seam integrating part of L2GSCI connects all the redundant
seams together to form a continuous and complete face curve. Overall, the
proposed method is much more straightforward than existing face alignment
algorithms, but can achieve pixel-level continuous face curves rather than
discrete and sparse landmarks. Moreover, experiments on two face benchmark
datasets (i.e., LFPW and HELEN) show that our method can precisely reveal
concave-convex bending details of face contours, which has significantly
improved the performance when compared with the state-ofthe- art face alignment
approaches.
