Generative adversarial learning network-based domain learning method

Abstract

The invention discloses a generative adversarial learning network-based domain learning method. The method comprises the following steps of 1) collecting a human face image set of a source domain, positioning human face positions by using a human face detector and extracting human face images; 2) constructing a generative neural network G, wherein input and output are the human face images with the same resolution, the input is the collected specific application scene human face image, and the output is the converted image; 3) constructing a classification neural network D, and applying neurons of a convolutional neural network, wherein the input is an output image of the generative neural network and a training image of a human face classifier, and the output is classification of two types of input images; 4) iteratively training the generative neural network G and the classification neural network D in an asynchronous mode; 5) when the human face recognition is carried out in a specified scene, performing conversion G(I) on the input human face image I, and inputting the converted human face image into a human face recognition module to obtain a human face recognition result. Themethod is relatively high in adaptability to a universal human face recognizer and relatively low in data cost.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and relates to a domain learning method based on a generative confrontation learning network. Background technique [0002] Applying classification models across domains is a major problem that limits the application of pattern recognition. In practical applications based on face recognition technology, such as witness comparison, face attendance, financial authentication, etc., due to the difference in image quality between the training data set of the face classifier and the actual application scene, face recognition technology The effect becomes worse in practical application. For specific application scenarios, it is time-consuming and labor-intensive to re-collect face images and manually label enough data, especially at present, the face recognition method based on deep learning generally requires a large number of training samples, so re-collecting face im...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the deficiencies of poor adaptability and high data cost in the prior art, the present invention provides a domain learning method based on generative adversarial learning network with strong adaptability and low data cost

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