Fine-grained facial landmark detection exploiting intermediate feature representations

Facial landmark detection has been an active research subject over the last decade. In this paper, we present a new approach for Fine-grained Facial Landmark Detection (FFLD) improving on the precision of the detected points. A high spatial precision of facial landmarks is crucial for many applications related to aesthetic rendering, such as face modeling, face animation, virtual make-up, etc. In this paper, we present an approach that improves the detection precision. Since most facial landmarks are positioned on visible boundary lines, we train a model that encourages the detected landmarks to stay on these boundaries. Our proposed Convolutional Neural Networks (CNN) effectively exploits lower-level feature maps containing abundant boundary information. To this end, beside the main CNN predicting facial landmark positions, we use several additional components, called CropNets. CropNet receives patches cropped from feature maps at different stages of this CNN, and estimate fine corrections of its predicted positions. We also introduce a novel robust spatial loss function based on pixel-wise differences between patches cropped from predicted and ground-truth positions. To further improve the landmark localization, our framework uses several loss functions optimizing the precision at several stages in different ways. Extensive experiments show that our framework significantly increases the local precision of state-of-the-art deep coordinate regression models.