基于改进型对抗网络的步态特征提取方研究

针对步态识别易受环境干扰等问题，本文以步态特征提取方法为研究重点，基于对抗学习网络框架提出了改进型姿态估计算法提取步态特征。该方法利用改进型残差网络获取由低层次到高层次的步态特征，随着网络层数的加深，对残差网络做出相应的调整，突出对局部细节特征信息的聚焦；同时设计了时序编码器，不仅提高了步态特征对于环境变化的泛化性，还减少了环境对特征提取的影响。最终在三种不同的实验模式下，基于CASIA数据集进行了大量的实验，识别精度均在83%以上，最终证明本文所提出的特征提取方法在复杂环境展现出良好的灵活性。

Research on gait feature extraction method based on improved generative adversarial networks

Aiming at the problem that gait recognition is vulnerable to environmental interference, this paper focuses on the gait feature extraction method, an improved attitude estimation algorithm is proposed based on the anti learning network framework to extract gait features. The improved residual network is used to obtain the gait features from low level to high level. With the deepening of the number of network layers, the residual network is adjusted accordingly to highlight the focus on the local detail feature information; A timing encoder is designed, which not only improves the generalization of gait features to environmental changes, but also reduces the impact of environment on feature extraction. Finally, a large number of experiments are carried out based on CASIA data set under three different experimental modes, the recognition accuracy is more than 83%, which finally proves that the feature extraction method proposed in this paper shows good flexibility in complex environment.