A Multi-Face Feature Point Localization Method Based on a Single Convolutional Neural Network

Abstract

The invention discloses a method for locating facial feature points based on a single convolutional neural network. The method is as follows: expanding training samples; determining the frame of the face according to the facial feature point coordinates corresponding to each sample provided by the data set; Rotate, translate, and flip four operations to expand data and make up for the lack of training image feature point labeling; extract face images according to the face bounding box, and perform normalization processing; finally design the network structure, train the network, and set the network The learning rate and the amount of data processed each time complete the positioning of multiple feature points on the face. This method simplifies the network structure and reduces the difficulty of training. This network structure can extract more global advanced features, express facial feature points more accurately, and have a good positioning effect on facial feature points under complex conditions. At the same time, it can realize Localization of multiple feature points on the face.

Description

technical field [0001] The invention relates to the field of biometric identification, in particular to a method for locating multiple facial feature points based on a single convolutional neural network. Background technique [0002] Facial landmark localization is an important problem in computer vision, because many vision tasks rely on accurate facial landmark localization results, such as face recognition, facial expression analysis, facial animation, etc. Although it has been extensively studied over the years, and with great success. However, due to the complex diversity of face images caused by factors such as partial occlusion, illumination, large head rotation, and exaggerated expression changes, facial landmark localization still faces great challenges. Convolutional neural networks have proven to be effective in feature extraction and classification, while it has also been shown to be robust against occlusions. [0003] Facial landmark localization methods are ...

AI Technical Summary

Problems solved by technology

However, multi-task learning is more demanding on datasets and complex training cannot be repeated

[0005] Obviously, the structure and training process of the above network are very complicated; secondly, most of the existing deep learning-based methods are aimed at positioning a small number of feature points.

When the number of feature points increases, the accuracy of positioning will become more difficult

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