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

## TL;DR

This paper introduces L2GSCI, a novel algorithm that extracts continuous, pixel-level face contours by local seam cutting and global seam integration, outperforming traditional landmark-based methods in detail and accuracy.

## Contribution

The paper presents a new local-to-global seam cutting and integrating algorithm for precise face contour extraction, improving detail capture over existing face alignment techniques.

## Key findings

- Achieves pixel-level continuous face contours.
- Outperforms state-of-the-art face alignment methods on benchmark datasets.
- Effectively captures concave-convex bending details of face contours.

## Abstract

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.