%0 Journal Article
%T HSANet：混合型自我注意力网络识别微整容人脸方法<br>HSANet: Hybrid Self-Attention Network Recognition Facial Micro Plastic Method
%A 帕孜来提?努尔买提
%A 古丽娜孜?艾力木江
%J Computer Science and Application
%P 301-310
%@ 2161-881X
%D 2023
%I Hans Publishing
%R 10.12677/CSA.2023.133029
%X <span style=\"font-size:14px;\">微整容给在日常生产中给人脸识别技术带来了新的挑战，因人脸特征变化较大导致对原人脸正确识别率较低，针对现象，该实验提出了一种混合型自我注意力块结构，用于识别面部特征变化的人脸，为此自制了26类微整容小样本图片数据集。将自我注意力融合到残差网络的瓶颈块中，提高了混合型自我注意力块对图片各区域特征的捕获能力，在对小样本微整容数据集的实验表明，该实验提出的混合型自我注意力网络有较高的正确识别率：89.70%，相比ResNet50正确识别率提高了2.65%，改进连接的混合型自我注意力模型比未改进连接的混合型自我注意力模型正确识别率提高了1.12%，网络性能也有所提升。</span><br />
<span style=\"font-size:14px;\">Due to the large changes in facial features, the correct recognition rate of the original face is low. In view of the phenomenon, this experiment proposed a hybrid self-attention block structure for rec-ognizing faces with facial features changes. For this reason, 26 kinds of micro-plastic surgery small sample image data sets were made by ourselves. Integrating self-attention into the bottleneck block of the residual network improves the ability of the hybrid self-attention block to capture the features of each region of the image. The experiment on the small sample micro-plastic data sets shows that the hybrid self-attention network proposed in this experiment has a higher correct recognition rate: 89.70%, the correct recognition rate increased by 2.65% compared with ResNet50, and the correct recognition rate of the hybrid selfattention model with improved connection increased by 1.12% compared with the hybrid self-attention model without improved connection, and the net-work performance was also improved.</span>
%K 卷积神经网络，残差网络，瓶颈块，自我注意力，混合型自我注意力网络<br>Convolutional Neural Networks
%K Residual Networks
%K Bottleneck Blocks
%K Self-Attention
%K Hybrid Self-Attention Network
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=62588