An End-to-End Face Alignment Method

Most of the existing face alignment methods are not end-to-end, and require frequent manual intervention, which leads to a reduction in their stability.To address the problem, an end-to-end face alignment method based on deep learning is proposed. The network required by this method is constructed based on the sub-modules of the MobileNet series in a structure similar to VGG.Taking the entire image as the input,the depth-wise separable convolution module is used for feature extraction, and the method employs an improved inverted residual structure to avoid the disappearance of gradients in the network training process while reducing the loss of features.The distance between eyes is taken as the basis for normalization. The designed network is tested on the 300W face dataset and compared with CDM, DRMF methods. The experimental results show that the proposed algorithm displays excellenst accuracy and real-time performance.