基于增强卷积神经网络的尺度不变人脸检测方法

针对非约束场景下小尺寸人脸检测困难的问题，提出了一种基于增强卷积神经网络的尺度不变人脸检测方法。首先，在SSD基础检测网络的两个浅层特征图上，通过协调聚合当前层特征图和前后两层特征图的特征信息，对当前层特征图的鉴别性和稳健性进行增强。然后，对两个增强特征图进行负样本筛选，通过增加分类的难度来降低由小尺寸锚框引起的人脸检测假正率上升。最后，为原始特征图和增强特征图设置了两种基于锚框尺寸的损失函数，并通过加权求和的方式对其进行融合。在FDDB和WIDER FACE数据集上的测试结果表明，文中所提方法比目前主流人脸检测方法具有更高的检测精度。

Scale-invariant face detection method based on enhanced convolutional neural network

Aiming at the difficulty of small-scale face detection in unconstrained scenes, the proposes a scale-invariant face detection method based on enhanced convolutional neural networks. Firstly, On the two shallow feature maps of the SSD basic detection network, the discrimination and robustness of the current layer feature map was enhanced by blending the feature information of the current layer feature map and adjacent layer feature map. Then, the negative sample screening was performed on the two enhanced feature maps, and the false positive rate of face detection caused by the small-scale anchor box was reduced by increasing the difficulty of classification. Finally, two loss function based on anchor boxes size were set for the original feature map and the enhanced feature map, and they were merged by weighted summation. The test results on the FDDB and WIDER FACE datasets show that the proposed method has higher detection accuracy than the current mainstream face detection methods.