Abstract
Facial landmark detection has been studied in recent years and has achieved good performance in controlled environment. However, the performance decreases significantly when face images are taken under wild conditions (e.g., different illuminations, occlusions, resolution and with different expressions and pose variations). Moreover, many methods need to determine face region before landmark detection. Therefore, the performance is affected by the accuracy of face detectors. The purpose of this work is to tackle the influence of environmental variations and ensure the detection accuracy even with instable face detectors. Therefore, we propose a two-level deep network to implement coarse-to-fine estimation. The first level predicts rough locations and the second level locally refines the results. We also adopt the multi-task learning into each level to include more information from face. Furthermore, we propose a CNN model to rectify inaccurate face region. Experimental results show that our approach uses fewer models to get more accurate results on AFLW and AFW datasets.