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.