  Facial landmark detection has been studied over decades. Numerous neural
network (NN)-based approaches have been proposed for detecting landmarks,
especially the convolutional neural network (CNN)-based approaches. In general,
CNN-based approaches can be divided into regression and heatmap approaches.
However, no research systematically studies the characteristics of different
approaches. In this paper, we investigate both CNN-based approaches, generalize
their advantages and disadvantages, and introduce a variation of the heatmap
approach, a pixel-wise classification (PWC) model. To the best of our
knowledge, using the PWC model to detect facial landmarks have not been
comprehensively studied. We further design a hybrid loss function and a
discrimination network for strengthening the landmarks' interrelationship
implied in the PWC model to improve the detection accuracy without modifying
the original model architecture. Six common facial landmark datasets, AFW,
Helen, LFPW, 300-W, IBUG, and COFW are adopted to train or evaluate our model.
A comprehensive evaluation is conducted and the result shows that the proposed
model outperforms other models in all tested datasets.
