基于汇聚CNN和注意力增强网络的遮挡人脸检测方法

Occlusion Face Detection Based on Convergent CNN and Attention EnhancementNetwork

针对现实场景中遮挡人脸检测精度低的问题,提出了一种基于汇聚CNN和注意力增强网络的遮挡人脸检测方法。首先,在主网络的多层原始特征图上,通过有监督学习的方法增强原始特征图中人脸可见部分的响应值。然后,将多个增强特征图组合成附加增强网络与主网络汇聚设置,以加快对多尺度遮挡人脸的检测速度。最后,将有监督信息分散到各个尺寸的特征图上进行监督学习,为不同尺寸的特征图设置了基于锚框尺寸的损失函数。在WIDER FACE和MAFA数据集上的实验结果表明,该方法的检测精度高于当前主流人脸检测方法。

Aiming at the problem of low detection accuracy of occluded faces in real scenes,an occluded face detection method based on convergent convolutional neural network(CNN)and attention enhancement network was proposed.First,on the multi-layer original feature map of the main network,the response value of the visible part of the face in the original feature map is enhanced by supervised learning.Then,multiple enhanced feature maps are combined into an additional enhanced network and set in converge with the main network to accelerate the detection of multi-scale occlusion faces.Finally,supervised information is distributed to feature maps of various sizes for supervised learning,and loss functions based on anchor frame sizes are set for feature maps of different sizes.Experimental results on WIDER FACE and MAFA datasets show that the detection accuracy of the proposed method is higher than the current mainstream face detection methods.