基于层级注意力增进网络的多尺寸遮挡人脸检测

Multi-size Occlusion Face Detection Based on Hierarchical Attention Enhancement Network

在SSD(Single shot multibox detector)单阶段人脸检测模型的基础上,针对复杂局部遮挡下人脸检测精确性差的问题,提出了一种基于层级注意力增进网络的多尺寸遮挡人脸检测方法。首先,在SSD基础网络的多层初始特征图上,通过引入注意力增进机制提升人脸可见区域的响应值。然后为不同增强特征层设计不同尺寸的锚框,以提高对多尺寸遮挡人脸的分层识别效果。最后在训练时将注意力损失函数、分类损失函数和回归损失函数融合为多任务损失函数,共同优化网络参数。在WIDER FACE人脸数据集和MAFA遮挡人脸数据集上的实验表明,本文方法的检测精确性和时效性均优于目前主流遮挡人脸检测方法。

Based on the single shot multibox detector(SSD)single-stage face detection model,this paper proposes a multi-size occlusion face detection method based on a hierarchical attention enhancement network to solve the problem of poor accuracy of face detection under complex partial occlusion.Firstly,on the multi-layer original feature map of SSD basic network,the attention enhancement mechanism is introduced to improve the response value of the visible region of the face.Then,different anchor sizes are designed for different enhancement feature layers to improve the hierarchical recognition effect of multi-scale occluded face.In training,the attention loss function,the classification loss function and the regression loss function are fused into a multi-task loss function to jointly optimize the network parameters.Experiments on the WIDER FACE dataset and the MAFA occlusion face dataset show that the detection accuracy and timeliness of the method are better than those of the current mainstream occlusion face detection methods.