Cross-view gait identification device based on dual-flow generation confrontation network and training method

Abstract

The invention belongs to the computer vision and mode identification field, particularly relates to a cross-view gait identification device based on the dual-flow generation confrontation network anda training method and aims to solve a problem of non-high cross-view gait identification accuracy. The method specifically comprises steps that a global flow gait energy image of a standard angle is learned through a global flow generation confrontation network model, and local flow gait energy images of standard angles are learned through utilizing three local flow generation confrontation network models. Global gait characteristics can be learned by a global flow model, on the basis of the global flow model, the local flow networks are added, so local gait characteristics can be learned; gait details can be restored through adding pixel-level constraints to a generator of the dual-flow generation confrontation network; through fusing the global gait characteristics and the local gait characteristics, gait identification accuracy can be improved. The method is advantaged in that quite strong robustness for the gait images is realized, and a cross-view gait identification problem can be better solved.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and in particular relates to a cross-view gait recognition device and a training method based on a dual-stream generative confrontation network. Background technique [0002] The problem of gait recognition is one of the most important problems in the fields of computer vision and biometric recognition. The change of viewing angle is the most influential factor on the accuracy of gait recognition. However, traditional gait recognition methods cannot obtain satisfactory results. With the development of deep learning, more and more researchers have applied deep learning to gait recognition recently. The two-stream generative adversarial network method proposed in this method can obtain better gait recognition accuracy. Contents of the invention [0003] In order to solve the above problems in the prior art, that is, to solve the problem of low accuracy of cross-view gait r...

AI Technical Summary

Problems solved by technology

The change of viewing angle is the most influential factor on the accuracy of gait recognition. However, traditional gait recognition methods cannot obtain satisfactory results.

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