Human face depth-prediction method based on convolution neural network

Abstract

The invention discloses a human face depth-prediction method based on convolution neural network, which comprises the following steps: generating a human face depth-prediction neural network in which a specially structured convolution neural network is created wherein the specially structured convolution neural network refers to the normalized operation and the excited operation in successive series-connection from the output end of the convolution layer, in series-connection with the aforementioned two operation convolution layers and in parallel to the KxK convolution; and initializing the convolution neural network in which the RGB human face images are inputted into the convolution neural network, and the minimized cost function is used for the iteration of the target so as to train and form the human face depth-prediction neural network.

Description

technical field [0001] The present invention relates to the fields of computer vision and digital image processing, in particular to a face depth prediction method based on a convolutional neural network. Background technique [0002] Facial depth prediction is a novel and challenging problem in computer vision. Depth prediction for faces is an important part of understanding the geometry of the whole face. Correspondingly, the geometric relationship of the face obtained in this way can well reflect the organs on the face and the environment of the whole face. If better depth information of the face can be obtained, it will provide a very large contribution to the face recognition problem. Help, but also help to build a 3D model of the face, and can also help solve the problem of 3D reconstruction of the face. [0003] However, predicting the depth information of the entire face from an RGB image of a face is a problem with a pathological nature, because there are many unc...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to propose a face depth prediction method based on convolutional neural network, to enhance the learning ability of convolutional neural network, and solve the gradient disappearance as the number of convolutional network layers increases in the aforementioned prior art problem, while improving the accuracy of depth prediction to obtain a clearer depth map

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