A divide-and-conquer strategy for facial landmark detection using dual-task CNN architecture

Highlights：

• We propose a novel deep learning-based framework for facial landmark detection (FLD).

• The proposed framework formulates the problem of FLD as a divide-conquer search for facial patches using CNN architecture in a hierarchy.

• A better division face topology is obtained by searching in a structured coarse-to-fine manner.

• A cascaded regressor is proposed to detect and refine the position of the individual landmark in each predicted non-overlapped patch.

• We compare our approach with many existing methods and we achieve the state-of-the-art performances on several challenging datasets.