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

L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction