Facial Landmark Detection based on Cascade Neural Network

A three-stage cascade convolutional neural network is proposed in this paper to learn the facial features. In order to improve the robustness and accuracy of facial landmark detection, several parallel sub-networks are involved in each stage of network. The convolutional neural network performs global advanced feature extraction for the entire facial region in the preceding stage, so as to obtain more accurate positioning. Since the first-stage input covers the whole image, the global facial information can be effectively used to weaken or even avoid the detection errors caused by occlusion, large posture changes, extreme illumination and etc. During facial detection, the first-stage network will make initial prediction of facial landmarks, and then get the facial area narrowed down and accurate according to the prediction results; the second-stage network will take the narrowed-down facial area above as the input, the three parallel convolution networks are used to classify the facial landmarks for quadratic estimates, so as to improve the accuracy of prediction results based on the results of first-stage network. The input of the third-stage network is limited to the small neighborhood of the second-stage prediction results, which can make the prediction results more accurate. The experimental data shows that our method has certain advantages in detection accuracy and reliability.