Single convolutional neural network-based facial multi-feature point locating method

Abstract

The invention discloses a single convolutional neural network-based facial multi-feature point locating method. The method comprises the steps of expanding training samples; according to facial feature point coordinates corresponding to the samples and provided by a data set, determining a human face boundary frame; expanding data by adopting four operations of zooming, rotation, translation and overturning to made up for the deficiency of feature point tagging of training images; according to the human face boundary frame, extracting a human face image, and performing normalization processing; and finally designing a network structure, training a network, setting a learning rate of the network and a data processing quantity each time, and finishing facial multi-feature point locating. According to the method, the network structure is simplified; the training difficulty is lowered; the network structure can extract more global advanced features and express facial feature points more accurately; the locating effect on the facial feature points under variable complex conditions is good; and the facial multi-feature point locating can be realized.

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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