当前，步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野，以融合局部特征，这种方法大多采用浅层网络，在提取步态图像的全局特征时存在一定的局限性，并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经网络算法（3D convolutional gait recognition network based on adaptFormer and spect-conv, 3D-ASgaitNet）。首先，初始残差卷积层将二进制轮廓数据转换为浮点编码特征图，以提供密集的低级结构特征；在此基础上，光谱层通过频域和时域的联合处理增强特征提取能力，并使用伪3D残差卷积模块进一步提取高级时空特征；最后融合AdaptFormer模块，通过轻量级的下采样-上采样网络结构，以适应不同的数据分布和任务需求，提供灵活的特征变换能力。3D-ASgaitNet分别在4个公开的室内数据集（CASIA-B、OU-MVLP）、室外数据集（GREW、Gait3D）上进行，分别取得99.84%、87.83%、45.32%、72.12%的识别准确率。实验结果表明，所提出方法在CASIA-B、Gait3D数据集中的识别准确率接近SOTA性能。

Currently, mainstream gait recognition methods often rely on stacked convolutional layers to gradually expand the receptive field and integrate local features. These methods mostly use shallow networks, which have limitations in extracting global features from gait images and lack attention to temporal cycle feature information. Therefore, a deep neural network algorithm combining Transformer and 3D convolution, named 3D convolutional gait recognition network based on AdaptFormer and Spect-Conv (3D-ASgaitNet) was proposed. Firstly, the initial residual convolution layer converts the binary contour data into a floating-point encoded feature map to provide dense low-level structural features. On this basis, the spectral layer enhances the feature extraction ability through the joint processing of frequency domain and time domain, and uses the pseudo-3D residual convolution module to further extract advanced spatio-temporal features. Finally, AdaptFormer module was integrated to provide flexible feature transformation capability through lightweight down-sampling and up-sampling network structure to adapt to different data distribution and task requirements. 3D-ASgaitNet was carried out on four publicly available indoor datasets (CASIA-B, OU-MVLP) and outdoor datasets (GREW, Gait3D), and achieved recognition accuracy rates of 99.84%, 87.83%, 45.32% and 72.12%, respectively. Experimental results show that the recognition accuracy of the proposed method in CASIA-B and Gait3D data sets is close to the performance of SOTA.