Face unsupervised feature learning method and device based on generative adversarial network

Abstract

The invention discloses a face unsupervised feature learning method and device based on a generative adversarial network. Through the preprocessing of a collected original face image, the image is converted into a face training image with a set size. The converted face training image is taken as training data to train a target generation network in a constructed depth convolution generative adversarial network. A generated random vector set is inputted into the trained target generation network, and a generative image set corresponding to the random vector set is obtained. The obtained generative image set is inputted into a depth regression network of a constructed depth convolutional neural network, the depth regression network is trained, and a face feature vector of the generative image set is extracted. The invention provides the face unsupervised feature learning method and device based on a generative adversarial network, a mode with the combination of DCGAN and DCNN is used to carry out unsupervised learning, the depth regression network is used to learn a reverse target generation network, the learning effect is good, and the identification accuracy is high.

Description

technical field [0001] The present invention relates to the technical field of face recognition, in particular, to a face unsupervised feature learning method and device based on a generative confrontation network. Background technique [0002] With the development of deep learning, the accumulation of Internet big data, and the development of hardware, the current face recognition technology has made a qualitative leap compared with 10 years ago, and is widely used in authentication fields such as security and finance. On the public data set IFW (Information Frame Work, information description framework), most companies can also reduce ERR (error) to less than 1%. However, most of the current face recognition algorithms based on deep learning are based on supervised learning and require a large number of labeled samples, such as a dataset of more than 50 samples per person for 20,000 people. The collection of data consumes a lot of manpower, material and financial resource...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a face non-supervised feature learning method and device based on a generative confrontation network to solve the problem that the existing face recognition algorithm using superv

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