Facial feature point positioning method based on end-to-end circulation network

Abstract

The invention discloses a facial feature point positioning method based on an end-to-end circulation network. The method includes the following steps: (1) a data preparation stage; (2) an end-to-end model designing stage; (3) a model training stage; and (4) a model testing stage to obtain a facial feature point positioning result of a face. Depth features of embedded shape information and a circulation neural network are cooperated, so the facial feature point positioning precision is greatly improved. In addition, through utilization of inherited neural network features, the model computational complexity will be greatly reduced, and the ultra-real-time facial feature point positioning speed is achieved while the good facial feature positioning precision is maintained.

Description

technical field [0001] The invention relates to a positioning method, in particular to a facial feature point positioning method based on an end-to-end recurrent network. Background technique [0002] The main goal of facial feature point positioning is to automatically locate key feature points of the face based on the input face, such as eyes, nose, mouth, and facial contours. This technology is widely used in face recognition, expression recognition, 3D reconstruction of face, and synthesis of face animation. Most of the current facial feature point localization methods are based on the deep neural network framework. The main methods are: [0003] 1) Coarse-to-fine facial feature point localization based on cascade structure or module division. Related patents: CN105981041A, CN105868769A. Its main technical means are: use multi-level model cascade and perform facial feature point positioning in a coarse-to-fine manner, and refine the position of facial feature points ...

AI Technical Summary

Problems solved by technology

Problems and shortcomings: This type of method is not robust to the facial feature point positioning of large-pose faces. The cascaded or sub-module framework is more sensitive to the given initial facial feature points. Once the initial facial feature point position is far away from the target position , the positioning of the final feature points will have a large deviation from the target; secondly, the cascaded framework is more likely to enter poor local optimization points during the training process, resulting in poor performance of the final model

Problems and disadvantages: Hard classification based on face angle is not necessarily the most suitable classification method for facial feature point positioning tasks, and this hard classification method may not be robust to facial feature point positioning of samples at category boundaries

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