Layer-wise supervised neural network for face alignment with multi-task regularization
Convolutional neural networks (CNN) have achieved prominent performance in facial landmark detection in recent years. However, the training of such deep network is non-trivial due to the over-fitting problem caused by the insufficient training data and the diminishing gradients problem occurred in the back-propagation. To address these problems, we propose a multi-task learning framework with supervised neural networks to jointly detect facial landmarks with a set of related tasks. On the one hand, to handle the over-fitting problem, the proposed method takes the advantage of additional task labels to train the model in a multi-task learning fashion to generate a shared feature representation for high-level recognition tasks. On the other hand, in order to tackle the transparency and diminishing gradients problem, the proposed method enforces supervision to the intermediate layers of the network, augmenting the gradient signal propagated from the final layer. Experiments on public benchmarks validate the effectiveness of the proposed method.