Detecting small faces in the wild based on generative adversarial network and contextual information

Highlights：

• A novel unified end-to-end convolutional neural network architecture for small face detection is proposed.

• A regression branch is introduced to the GAN-based architecture for further refining the locations of small faces in the wild.

• New losses are designed to train the GAN-based network for small face detection in the wild.

• Contextual information around face regions is further utilized to detect hard faces in the real-world scenarios.

• The performance of our method outperforms previous state-of-the-art approaches by a large margin on WIDER FACE dataset, especially on the most challenging Hard subset.