Human face attribute prediction method and apparatus based on deep study and multi-task study

Abstract

The invention discloses a human face attribute prediction method and apparatus based on deep study and multi-task study. The face attribute prediction method mainly comprises: collecting a human face, marking a category corresponding to a plurality of attributes, and forming a training data set; detecting a human face and key points of the human face, and aligning the human face via a plurality of key points; encoding the sequential attributes in the category; constructing a deep neural network; and using the training data set to train the deep neural network; deploying a neural network model obtained via training; and finally, using the neural network model to predict the human face attribute in a picture. According to the human face attribute prediction method provided by the invention, through united training of a plurality of attributes, a plurality of attributes can be predicted simultaneously with only one deep network, and a prediction result is improved obviously.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a face attribute prediction method and device based on deep learning and multi-task learning. Background technique [0002] At present, predicting face attributes from face images has attracted more and more attention. Face attributes include expression, action unit, gender, age, race, mouth size, nose bridge height, whether to wear glasses, whether to wear sunglasses, eye size, eyes open or closed, mouth open or closed, hair length or Hair style, face value, front or side view, etc. Face attribute prediction technology is now widely used in human-computer interaction, user modeling and other fields. [0003] The existing face attribute prediction is mainly based on the traditional machine learning framework. First, the artificially designed features are extracted, and then the dimensionality of the features is reduced to obtain compact features. Finally, the classificat...

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Problems solved by technology

[0009] In order to solve the problems that the results of the face attribute prediction method in the prior art are not good enough, the calculation process is complicated, and the attribute value expression ability is not strong, the present invention proposes a face attribute prediction method based on deep learning and multi-task learning

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