Generative adversarial network-based multi-pose face generation method

Abstract

The present invention discloses a generative adversarial network-based multi-pose face generation method. According to the generative adversarial network-based multi-pose face generation method, in a training phase, the face data of various poses are collected; two deep neural networks G and D are trained on the basis of a generative adversarial network; and after training is completed, the generative network G is inputted on the basis of random sampling and pose control parameters, so that face images of various poses can be obtained. With the method of the invention adopted, a large quantity of different face images of a plurality of poses can be generated, and the problem of data shortage in the multi-pose face recognition field can be solved; the newly generated face images of various poses are adopted as training data to train an encoder for extracting the identity information of the images; in a final testing process, an image of a random pose is inputted, and identity information features are obtained through the trained encoder; and the face images of various poses of the same person are obtained through the trained generative network.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and in particular relates to deep learning, generation confrontation model (GAN) and traditional image processing methods. Background technique [0002] In recent years, with the in-depth development of big data technology, biometric identification has become an important research direction in the field of information security. As the most active branch of biometric recognition, face recognition has been full of vitality in recent years. Since 2013, with the development of deep learning, deep neural networks have been gradually applied to the field of face recognition, and have achieved higher accuracy than traditional recognition methods. Although face recognition algorithms based on deep learning have made great progress compared with traditional algorithms, most current face recognition systems assume a single limited scene, that is, a scene with controllable lighting en...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the difficult problem of multi-pose face recognition, especially the lack of large-scale multi-pose face database, the present invention provides a multi-pose face generation method based on generative confrontation network

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